Latest open access articles published in Precision Mechanics & Digital Fabrication at https://www.acadlore.com/journals/PMDF https://www.acadlore.com/journals/PMDF Acadlore en Creative Commons Attribution(CC - BY) PMDF support@acadlore.com https://www.acadlore.com/article/PMDF/2025_2_4/pmdf020403 The long-term durability of reinforced concrete infrastructure remains a critical challenge, as conventional Portland cement and carbon steel systems are inherently vulnerable to corrosion and environmental degradation. Roman concrete demonstrates exceptional longevity due to slow hydration kinetics, pozzolanic reactions, and self-healing mechanisms, but its integration into modern construction is limited by incompatibility with rapid construction workflows. At the same time, additive manufacturing has enabled advanced geometric control, while rarely addressing durability as a primary design objective. This study proposes a durability-driven construction system integrating Roman-type concrete, stainless steel reinforcement, and permanent additively manufactured thermoplastic formworks. Rather than acting as a temporary construction aid, the formwork is redefined as a permanent protective enclosure that sustains early-age loads, accommodates slow curing, and provides long-term environmental shielding. Stainless steel reinforcement is employed to mitigate corrosion, the dominant degradation mechanism. The system is evaluated using a multi-level methodology that combines material compatibility analysis, finite-element modelling of early-age conditions, and architectural-scale demonstration. The critical pre- and post-casting phases are analysed by modelling the fresh concrete as a fluid-like load acting on the permanent formwork, which represents the load-bearing component prior to setting. A segmented dome inspired by the Pantheon is used to demonstrate scalability and system integration. While direct validation over century-scale timeframes is impractical, the results show that the proposed system satisfies necessary conditions for extended service life, providing a scientifically grounded framework for durability-oriented construction using additive manufacturing. 11-18-2025 <![CDATA[ The long-term durability of reinforced concrete infrastructure remains a critical challenge, as conventional Portland cement and carbon steel systems are inherently vulnerable to corrosion and environmental degradation. Roman concrete demonstrates exceptional longevity due to slow hydration kinetics, pozzolanic reactions, and self-healing mechanisms, but its integration into modern construction is limited by incompatibility with rapid construction workflows. At the same time, additive manufacturing has enabled advanced geometric control, while rarely addressing durability as a primary design objective. This study proposes a durability-driven construction system integrating Roman-type concrete, stainless steel reinforcement, and permanent additively manufactured thermoplastic formworks. Rather than acting as a temporary construction aid, the formwork is redefined as a permanent protective enclosure that sustains early-age loads, accommodates slow curing, and provides long-term environmental shielding. Stainless steel reinforcement is employed to mitigate corrosion, the dominant degradation mechanism. The system is evaluated using a multi-level methodology that combines material compatibility analysis, finite-element modelling of early-age conditions, and architectural-scale demonstration. The critical pre- and post-casting phases are analysed by modelling the fresh concrete as a fluid-like load acting on the permanent formwork, which represents the load-bearing component prior to setting. A segmented dome inspired by the Pantheon is used to demonstrate scalability and system integration. While direct validation over century-scale timeframes is impractical, the results show that the proposed system satisfies necessary conditions for extended service life, providing a scientifically grounded framework for durability-oriented construction using additive manufacturing. ]]> Durability-Oriented Construction Using Roman-Type Concrete and Permanent 3D-Printed Formworks luca piancastelli gian maria santi andrea montalti doi: 10.56578/pmdf020403 Precision Mechanics & Digital Fabrication 11-18-2025 Precision Mechanics & Digital Fabrication 11-18-2025 2025 2 4 Article 234 10.56578/pmdf020403 https://www.acadlore.com/article/PMDF/2025_2_4/pmdf020403 https://www.acadlore.com/article/PMDF/2025_2_4/pmdf020402 To address the inadequate accuracy in chatter detection during milling operations, this study proposes a novel milling chatter detection methodology based on an optimized iTransformer-BiGRU-Random Forest (iTBU-RF) hybrid model. Initially, sensitivity analysis of time-frequency domain features is conducted employing Pearson correlation coefficients and significance levels to identify the features most sensitive to chatter detection. Subsequently, a chatter detection model integrating iTBU and RF is constructed. The hyperparameters of the ensemble model are optimized through the Ivy optimization algorithm. Following hyperparameter optimization, the model’s accuracy is substantially enhanced, achieving a maximum improvement of 2.40% compared to the pre-optimized configuration. Upon feature optimization, the model maintains superior classification performance while simultaneously reducing training time from 153.83 seconds to 116.74 seconds, thereby improving computational efficiency by approximately 24.11%. In comparison with benchmark methodologies, the proposed approach demonstrates optimal performance across all evaluation metrics, including accuracy. This investigation provides a novel technological framework for enhancing the precision of chatter detection in milling operations. 10-15-2025 <![CDATA[ <p>To address the inadequate accuracy in chatter detection during milling operations, this study proposes a novel milling chatter detection methodology based on an optimized iTransformer-BiGRU-Random Forest (iTBU-RF) hybrid model. Initially, sensitivity analysis of time-frequency domain features is conducted employing Pearson correlation coefficients and significance levels to identify the features most sensitive to chatter detection. Subsequently, a chatter detection model integrating iTBU and RF is constructed. The hyperparameters of the ensemble model are optimized through the Ivy optimization algorithm. Following hyperparameter optimization, the model’s accuracy is substantially enhanced, achieving a maximum improvement of 2.40% compared to the pre-optimized configuration. Upon feature optimization, the model maintains superior classification performance while simultaneously reducing training time from 153.83 seconds to 116.74 seconds, thereby improving computational efficiency by approximately 24.11%. In comparison with benchmark methodologies, the proposed approach demonstrates optimal performance across all evaluation metrics, including accuracy. This investigation provides a novel technological framework for enhancing the precision of chatter detection in milling operations.</p> ]]> Milling Chatter Detection Based on an Optimized iTransformer-BiGRU-Random Forest Ensemble Model hongdan shen haining gao lin yang rongyi li yang yong shule xing doi: 10.56578/pmdf020402 Precision Mechanics & Digital Fabrication 10-15-2025 Precision Mechanics & Digital Fabrication 10-15-2025 2025 2 4 Article 222 10.56578/pmdf020402 https://www.acadlore.com/article/PMDF/2025_2_4/pmdf020402 https://www.acadlore.com/article/PMDF/2025_2_4/pmdf020401 3D printing provides an effective digital fabrication route for manufacturing structured adsorbents with customized geometries, offering clear advantages in permeability, recoverability, and structural integration for water treatment applications. However, a fundamental challenge remains: high porosity, which is essential for mass transfer and adsorption capacity, often compromises mechanical robustness, thereby limiting structural stability, recyclability, and service life under dynamic operating conditions. Most existing studies address this trade-off through incremental optimization within individual material systems, resulting in limited performance improvement. This review systematically summarizes recent advances in 3D-printed structured adsorbents by taking adsorption mechanisms as the central framework. Strategies for enhancing mass transfer through hierarchical pore architecture are reviewed alongside a critical analysis of chemical durability and mechanically governed structural stability, which are key factors for engineering reliability. Emerging fabrication approaches, including core-shell printing and multi-material co-extrusion, are discussed as promising routes to decouple adsorption functionality from load-bearing structures, enabling the concurrent improvement of adsorption performance and mechanical integrity. In addition, challenges related to performance evaluation, dynamic adsorption testing, and cost-benefit considerations are examined, providing guidance for the transition from material-level printing toward structurally reliable adsorption device design. 10-01-2025 <![CDATA[ 3D printing provides an effective digital fabrication route for manufacturing structured adsorbents with customized geometries, offering clear advantages in permeability, recoverability, and structural integration for water treatment applications. However, a fundamental challenge remains: high porosity, which is essential for mass transfer and adsorption capacity, often compromises mechanical robustness, thereby limiting structural stability, recyclability, and service life under dynamic operating conditions. Most existing studies address this trade-off through incremental optimization within individual material systems, resulting in limited performance improvement. This review systematically summarizes recent advances in 3D-printed structured adsorbents by taking adsorption mechanisms as the central framework. Strategies for enhancing mass transfer through hierarchical pore architecture are reviewed alongside a critical analysis of chemical durability and mechanically governed structural stability, which are key factors for engineering reliability. Emerging fabrication approaches, including core-shell printing and multi-material co-extrusion, are discussed as promising routes to decouple adsorption functionality from load-bearing structures, enabling the concurrent improvement of adsorption performance and mechanical integrity. In addition, challenges related to performance evaluation, dynamic adsorption testing, and cost-benefit considerations are examined, providing guidance for the transition from material-level printing toward structurally reliable adsorption device design. ]]> 3D-Printed Structured Adsorbents for Wastewater Treatment: Balancing Hierarchical Pore Design and Mechanical Robustness tanghui ding lu zhang doi: 10.56578/pmdf020401 Precision Mechanics & Digital Fabrication 10-01-2025 Precision Mechanics & Digital Fabrication 10-01-2025 2025 2 4 Article 189 10.56578/pmdf020401 https://www.acadlore.com/article/PMDF/2025_2_4/pmdf020401 https://www.acadlore.com/article/PMDF/2025_2_3/pmdf020305 The expensive energy prices and sustainability goals are driving the precision manufacturing facilities to stop their periodic energy reporting to full-time, machine-level reporting that can provide insights into where energy is used, anticipate future-demand and observe unusual behavior in the CNC machining and digital fabrication processes. This paper creates a real-time smart power dashboard, which combines power measurement and production-aware processing to facilitate actionable energy governance on the shop floor. This workflow coordinates time-stamped power data (and optional machine context), cleanses and rebuilds windows of features, and uses a multi-model forecasting layer (autoregressive integrated moving average, additive time-series decomposition, gradient-boosted regression, and long short-term memory (LSTM)) to make short-horizon predictions. A dual protocol based on standardized deviation monitoring and isolation-based outlier detectors detect abnormal consumption with energy windows being clustered into repeatable profiles using clustering to facilitate benchmarking across machines and shifts. The prototype testing demonstrates that the forecasting layer has a best mean absolute percentage error (MAPE) of 8.9, the clustering operation has a conspicuous separation with a silhouette score of 0.742 and the anomaly detection has a precision of 95.7 and a false positive of 2.8 at minimal computing power. Such findings show that the dashboard, as suggested, can be used to provide reliable forecasting, interpretable profiling and low noise alerting that can be used in real-time monitoring. The strategy offers deployable analytics structure that converts raw power streams into decision-ready data and facilitates undertakable efficiency steps by means of energy per job, peak-load exposure, and share of non-productive energy indicators. 09-25-2025 <![CDATA[ <p>The expensive energy prices and sustainability goals are driving the precision manufacturing facilities to stop their periodic energy reporting to full-time, machine-level reporting that can provide insights into where energy is used, anticipate future-demand and observe unusual behavior in the CNC machining and digital fabrication processes. This paper creates a real-time smart power dashboard, which combines power measurement and production-aware processing to facilitate actionable energy governance on the shop floor. This workflow coordinates time-stamped power data (and optional machine context), cleanses and rebuilds windows of features, and uses a multi-model forecasting layer (autoregressive integrated moving average, additive time-series decomposition, gradient-boosted regression, and long short-term memory <span style="color: rgb(0, 0, 0); font-family: Times New Roman, sans-serif">(LSTM</span>)) to make short-horizon predictions. A dual protocol based on standardized deviation monitoring and isolation-based outlier detectors detect abnormal consumption with energy windows being clustered into repeatable profiles using clustering to facilitate benchmarking across machines and shifts. The prototype testing demonstrates that the forecasting layer has a best mean absolute percentage error <span style="color: rgb(0, 0, 0); font-family: Times New Roman, sans-serif">(MAPE)</span> of 8.9, the clustering operation has a conspicuous separation with a silhouette score of 0.742 and the anomaly detection has a precision of 95.7 and a false positive of 2.8 at minimal computing power. Such findings show that the dashboard, as suggested, can be used to provide reliable forecasting, interpretable profiling and low noise alerting that can be used in real-time monitoring. The strategy offers deployable analytics structure that converts raw power streams into decision-ready data and facilitates undertakable efficiency steps by means of energy per job, peak-load exposure, and share of non-productive energy indicators.</p> ]]> Multi-Model AI-Driven Smart Energy Dashboard for Real-Time Monitoring of CNC and Digital Fabrication Energy Consumption amit aylani pushkar uikey doi: 10.56578/pmdf020305 Precision Mechanics & Digital Fabrication 09-25-2025 Precision Mechanics & Digital Fabrication 09-25-2025 2025 2 3 Article 178 10.56578/pmdf020305 https://www.acadlore.com/article/PMDF/2025_2_3/pmdf020305 https://www.acadlore.com/article/PMDF/2025_2_3/pmdf020304 This work examines how different welding regimes and filler metal types influence the characteristics of hardfaced layers and the associated heat-affected zones (HAZ) in components made of low-alloy steel 30CrMoV9. Bead-on-plate welding tests were carried out on plate specimens, using five filler metals, including four gas-shielded wires with different chemical compositions and one flux-cored wire. For each filler metal, two welding regimes were applied by varying the current, voltage, and travel speed. After welding, the bead geometry and hardness were measured, and bending tests were performed to assess cracking behavior. The results show that both filler metal selection and arc energy have a pronounced effect on bead shape and hardness, as well as on the hardness distribution in the HAZ. It is also observed that, because of the metallurgical characteristics of 30CrMoV9 steel, preheating and/or post-weld heat treatment is required to reduce the risk of cracking. The findings may serve as practical input for process selection and quality control in the fabrication and repair of precision mechanical parts. 08-31-2025 <![CDATA[ This work examines how different welding regimes and filler metal types influence the characteristics of hardfaced layers and the associated heat-affected zones (HAZ) in components made of low-alloy steel 30CrMoV9. Bead-on-plate welding tests were carried out on plate specimens, using five filler metals, including four gas-shielded wires with different chemical compositions and one flux-cored wire. For each filler metal, two welding regimes were applied by varying the current, voltage, and travel speed. After welding, the bead geometry and hardness were measured, and bending tests were performed to assess cracking behavior. The results show that both filler metal selection and arc energy have a pronounced effect on bead shape and hardness, as well as on the hardness distribution in the HAZ. It is also observed that, because of the metallurgical characteristics of 30CrMoV9 steel, preheating and/or post-weld heat treatment is required to reduce the risk of cracking. The findings may serve as practical input for process selection and quality control in the fabrication and repair of precision mechanical parts. ]]> Influence of Welding Regimes and Filler Metals on Hardfaced Layers for Precision Mechanical Components Made of 30CrMoV9 Steel svetislav lj. marković doi: 10.56578/pmdf020304 Precision Mechanics & Digital Fabrication 08-31-2025 Precision Mechanics & Digital Fabrication 08-31-2025 2025 2 3 Article 172 10.56578/pmdf020304 https://www.acadlore.com/article/PMDF/2025_2_3/pmdf020304 https://www.acadlore.com/article/PMDF/2025_2_3/pmdf020303 The fundamental mechanical properties and constrained recovery behavior of two domestically produced Fe-Mn-Si shape memory alloys (SMAs) (Fe-16.86Mn-4.5Si-10.3Cr-5.29Ni-0.08C and Fe-17.6Mn-4.5Si-3.22Cr-2.96Ni-0.28C-1.45V) were investigated with specific reference to their potential application in bridge strengthening. Uniaxial tensile tests, differential scanning calorimetry (DSC), and thermal expansion measurements were conducted to determine the elastic modulus, transformation stress, transformation temperatures, and thermal expansion characteristics. The alloy containing vanadium exhibited a higher elastic modulus and a higher transformation stress than the vanadium-free alloy. In addition, the presence of vanadium significantly reduced the width of the transformation temperature interval, which is advantageous for temperature control during practical activation. Constrained recovery tests showed that the recovery stress increased with increasing activation temperature and reached a maximum at a pre-strain of approximately 6%. The level of pre-applied stress had only a minor effect on the final recovery stress, indicating a stable and controllable recovery behavior under engineering conditions. These results provide both experimental data and a mechanical basis for the use of domestically produced Fe-Mn-Si shape memory alloys in the active strengthening of civil engineering structures. 08-17-2025 <![CDATA[ <p>The fundamental mechanical properties and constrained recovery behavior of two domestically produced Fe-Mn-Si shape memory alloys (SMAs) (Fe-16.86Mn-4.5Si-10.3Cr-5.29Ni-0.08C and Fe-17.6Mn-4.5Si-3.22Cr-2.96Ni-0.28C-1.45V) were investigated with specific reference to their potential application in bridge strengthening. Uniaxial tensile tests, differential scanning calorimetry (DSC), and thermal expansion measurements were conducted to determine the elastic modulus, transformation stress, transformation temperatures, and thermal expansion characteristics. The alloy containing vanadium exhibited a higher elastic modulus and a higher transformation stress than the vanadium-free alloy. In addition, the presence of vanadium significantly reduced the width of the transformation temperature interval, which is advantageous for temperature control during practical activation. Constrained recovery tests showed that the recovery stress increased with increasing activation temperature and reached a maximum at a pre-strain of approximately 6%. The level of pre-applied stress had only a minor effect on the final recovery stress, indicating a stable and controllable recovery behavior under engineering conditions. These results provide both experimental data and a mechanical basis for the use of domestically produced Fe-Mn-Si shape memory alloys in the active strengthening of civil engineering structures.</p> ]]> Mechanical Characterization and Constrained Recovery Behavior of Domestically Produced Fe-Mn-Si Shape Memory Alloys for Structural Strengthening honglei ma yifeng zheng doi: 10.56578/pmdf020303 Precision Mechanics & Digital Fabrication 08-17-2025 Precision Mechanics & Digital Fabrication 08-17-2025 2025 2 3 Article 162 10.56578/pmdf020303 https://www.acadlore.com/article/PMDF/2025_2_3/pmdf020303 https://www.acadlore.com/article/PMDF/2025_2_3/pmdf020302 The design of automatic clamping mechanisms often involves trade-offs between clamping stability, structural compactness, manufacturability, and operational reliability. These trade-offs are difficult to handle in early design stages, where decisions are largely experience-based and design alternatives are not yet fully defined. An integrated design approach combining Extenics and TRIZ is applied to support the innovative development and structural optimization of an automatic clamping mechanism. Functional requirements and structural constraints are first expressed in the form of Extenics element models. Key design conflicts are then identified through functional analysis and addressed using TRIZ contradiction principles and inventive principles, which guide the generation of alternative structural configurations. The candidate designs are evaluated with respect to mechanical performance, manufacturability, and structural feasibility in order to select a configuration that better satisfies practical engineering requirements. The approach is illustrated through the redesign of an automatic clamping mechanism. The results show that the selected configuration improves clamping stability and structural reliability while maintaining reasonable manufacturability. The study suggests that the combined use of Extenics and TRIZ can support systematic innovation in mechanical structure design and provide practical guidance for similar precision engineering applications. 07-14-2025 <![CDATA[ The design of automatic clamping mechanisms often involves trade-offs between clamping stability, structural compactness, manufacturability, and operational reliability. These trade-offs are difficult to handle in early design stages, where decisions are largely experience-based and design alternatives are not yet fully defined. An integrated design approach combining Extenics and TRIZ is applied to support the innovative development and structural optimization of an automatic clamping mechanism. Functional requirements and structural constraints are first expressed in the form of Extenics element models. Key design conflicts are then identified through functional analysis and addressed using TRIZ contradiction principles and inventive principles, which guide the generation of alternative structural configurations. The candidate designs are evaluated with respect to mechanical performance, manufacturability, and structural feasibility in order to select a configuration that better satisfies practical engineering requirements. The approach is illustrated through the redesign of an automatic clamping mechanism. The results show that the selected configuration improves clamping stability and structural reliability while maintaining reasonable manufacturability. The study suggests that the combined use of Extenics and TRIZ can support systematic innovation in mechanical structure design and provide practical guidance for similar precision engineering applications. ]]> Innovative Design and Structural Optimization of an Automatic Clamping Mechanism Integrating Extenics and TRIZ fan jiang wenxi chen jietao dai doi: 10.56578/pmdf020302 Precision Mechanics & Digital Fabrication 07-14-2025 Precision Mechanics & Digital Fabrication 07-14-2025 2025 2 3 Article 146 10.56578/pmdf020302 https://www.acadlore.com/article/PMDF/2025_2_3/pmdf020302 https://www.acadlore.com/article/PMDF/2025_2_3/pmdf020301 The enduring resilience of Roman infrastructure, exemplified by the Tiberius Bridge in Rimini—completed in the 1st century CE and remaining structurally sound after over two millennia—has long drawn scholarly attention. This study re-evaluates Roman construction methodologies with a particular focus on opus caementicium (Roman concrete) encased within durable permanent facings such as opus quadratum, opus incertum, and opus latericium. Central to this longevity was the use of pozzolanic binders, which underwent prolonged hydration reactions, enabling continued strength development over extended timescales—markedly contrasting with contemporary hydraulic cements engineered for rapid early-age strength gain. A comparative analysis is conducted between ancient Roman materials and modern high-performance cementitious composites, including High-Performance Concrete (HPC), Ultra-High Performance Concrete (UHPC), and Engineered Cementitious Composites (ECC). Contemporary practices are frequently guided by design codes such as Eurocode, which, while structurally robust, tend to prioritize short-term performance metrics. To bridge this gap, a hybrid construction strategy is proposed wherein additive manufacturing is employed to produce permanent structural formworks that mimic the load-bearing and protective functions of Roman facings. This approach enables the use of modern slow-maturing binders within digitally fabricated enclosures, thus integrating ancient durability principles into automated, scalable workflows. By reconciling historical construction insights with advances in modern materials science and digital fabrication, a new paradigm is introduced for designing infrastructure with service lives far exceeding the conventional 50–100 year design horizon. The implications of such an approach extend to both sustainability and resilience, offering a technically viable and historically informed route toward ultra-durable infrastructure in the face of evolving environmental and operational challenges. 07-04-2025 <![CDATA[ <p>The enduring resilience of Roman infrastructure, exemplified by the Tiberius Bridge in Rimini—completed in the 1st century CE and remaining structurally sound after over two millennia—has long drawn scholarly attention. This study re-evaluates Roman construction methodologies with a particular focus on <em>opus caementicium</em> (Roman concrete) encased within durable permanent facings such as <em>opus quadratum</em>, <em>opus incertum</em>, and <em>opus latericium</em>. Central to this longevity was the use of pozzolanic binders, which underwent prolonged hydration reactions, enabling continued strength development over extended timescales—markedly contrasting with contemporary hydraulic cements engineered for rapid early-age strength gain. A comparative analysis is conducted between ancient Roman materials and modern high-performance cementitious composites, including High-Performance Concrete (HPC), Ultra-High Performance Concrete (UHPC), and Engineered Cementitious Composites (ECC). Contemporary practices are frequently guided by design codes such as Eurocode, which, while structurally robust, tend to prioritize short-term performance metrics. To bridge this gap, a hybrid construction strategy is proposed wherein additive manufacturing is employed to produce permanent structural formworks that mimic the load-bearing and protective functions of Roman facings. This approach enables the use of modern slow-maturing binders within digitally fabricated enclosures, thus integrating ancient durability principles into automated, scalable workflows. By reconciling historical construction insights with advances in modern materials science and digital fabrication, a new paradigm is introduced for designing infrastructure with service lives far exceeding the conventional 50–100 year design horizon. The implications of such an approach extend to both sustainability and resilience, offering a technically viable and historically informed route toward ultra-durable infrastructure in the face of evolving environmental and operational challenges.</p> ]]> Reassessing Roman Structural Longevity: From Firmitas to Eurocode Through the Lens of Modern Cementitious Technologies luca piancastelli enrico lorenzini doi: 10.56578/pmdf020301 Precision Mechanics & Digital Fabrication 07-04-2025 Precision Mechanics & Digital Fabrication 07-04-2025 2025 2 3 Article 134 10.56578/pmdf020301 https://www.acadlore.com/article/PMDF/2025_2_3/pmdf020301 https://www.acadlore.com/article/PMDF/2025_2_2/pmdf020205 Prompt and proper maintenance management helps extend the operation lifespan of workplace equipment to achieve production targets without interrupting the production process. In this connection, accurate prediction of the reliability-based scheduled maintenance (SM) time intervals of equipment is essential. The current research aimed to develop a reliability-based model to forecast the maintenance time intervals specifically for Load-Haul-Dumper (LHD) underground mining equipment. The series configuration system of the Reliability Block Diagram (RBD) model was adopted to evaluate the overall system reliability for each LHD machine. The reliability percentage of each sub-system was ascertained through a reliability analysis of a complex repairable system. To build the required Artificial Neural Network (ANN) model for analysis, the “Isograph Reliability Workbench 13.0” software was adopted to estimate the input layers of reliability ($R$) and the best-fit distribution parameters, such as the scale parameter ($\eta$), shape parameter ($\beta$), and location parameter ($\gamma$). The ANN model created was trained using the Levenberg-Marquardt (LM) learning algorithm. The predicted SM values were extremely close to the calculated values as indicated by the optimal $R^2$ value of 0.9998. The outcome demonstrated that the ANN model could improve the performance of the equipment with a major impact on the initial weight optimization. Suggestions were made for the industry practitioners to enhance the dependability of the equipment with planned maintenance procedures designed by the proposed ANN, with possible potential to be explored by other equipment users. 06-29-2025 <![CDATA[ Prompt and proper maintenance management helps extend the operation lifespan of workplace equipment to achieve production targets without interrupting the production process. In this connection, accurate prediction of the reliability-based scheduled maintenance (SM) time intervals of equipment is essential. The current research aimed to develop a reliability-based model to forecast the maintenance time intervals specifically for Load-Haul-Dumper (LHD) underground mining equipment. The series configuration system of the Reliability Block Diagram (RBD) model was adopted to evaluate the overall system reliability for each LHD machine. The reliability percentage of each sub-system was ascertained through a reliability analysis of a complex repairable system. To build the required Artificial Neural Network (ANN) model for analysis, the “Isograph Reliability Workbench 13.0” software was adopted to estimate the input layers of reliability ($R$) and the best-fit distribution parameters, such as the scale parameter ($\eta$), shape parameter ($\beta$), and location parameter ($\gamma$). The ANN model created was trained using the Levenberg-Marquardt (LM) learning algorithm. The predicted SM values were extremely close to the calculated values as indicated by the optimal $R^2$ value of 0.9998. The outcome demonstrated that the ANN model could improve the performance of the equipment with a major impact on the initial weight optimization. Suggestions were made for the industry practitioners to enhance the dependability of the equipment with planned maintenance procedures designed by the proposed ANN, with possible potential to be explored by other equipment users. ]]> Reliability-Based Scheduled Maintenance (SM) for Mining Equipment with Artificial Neural Network (ANN) Model balaraju jakkula govinda raj mandela anup kumar tripathi doi: /10.56578/pmdf020205 Precision Mechanics & Digital Fabrication 06-29-2025 Precision Mechanics & Digital Fabrication 06-29-2025 2025 2 2 Article 124 /10.56578/pmdf020205 https://www.acadlore.com/article/PMDF/2025_2_2/pmdf020205 https://www.acadlore.com/article/PMDF/2025_2_2/pmdf020204 The influence of prestrain on the microstructural evolution and corrosion behaviour of copper-based alloys has been systematically investigated to elucidate the mechanisms by which mechanical preconditioning enhances structural integrity and electrochemical stability. Prestrain, applied prior to subsequent thermomechanical treatments, has been found to significantly alter dislocation density, grain size distribution, phase transformation pathways, and precipitate morphology and distribution. These changes collectively promote grain refinement and the formation of nanocrystalline domains, thereby improving both strength and ductility. Enhanced effects have been observed in Cu–Cr–Zr and Cu–Al–Ni alloys, particularly when prestrain is introduced via cold rolling or friction stir processing (FSP). In these systems, microstructural stability during post-deformation ageing is markedly improved due to the suppression of grain coarsening and the controlled precipitation of strengthening phases. Moreover, prestrain modifies the local chemical and crystallographic environment in a manner that critically impacts electrochemical behavior. Intermediate levels of mechanical stress have been shown to improve corrosion resistance by facilitating the formation of uniform, adherent passive films, while excessive strain introduces microstructural heterogeneities that serve as initiation sites for intergranular and stress corrosion cracking. These phenomena were characterized using X-ray diffraction, scanning and transmission electron microscopy (TEM), and electrochemical techniques including potentiodynamic polarization and electrochemical impedance spectroscopy. The interplay between mechanical preconditioning, microstructural refinement, and corrosion mechanisms has been clarified, offering insights into process–structure–property relationships. The findings hold particular relevance for the design and optimization of copper alloys in high-performance applications such as electronic interconnects, biomedical implants, and aerospace components, where dimensional stability, chemical resilience, and machinability are of paramount importance. The study underscores the critical role of prestrain not only as a structural refinement tool but also as a means of tailoring corrosion resistance through controlled microstructural engineering. 06-29-2025 <![CDATA[ The influence of prestrain on the microstructural evolution and corrosion behaviour of copper-based alloys has been systematically investigated to elucidate the mechanisms by which mechanical preconditioning enhances structural integrity and electrochemical stability. Prestrain, applied prior to subsequent thermomechanical treatments, has been found to significantly alter dislocation density, grain size distribution, phase transformation pathways, and precipitate morphology and distribution. These changes collectively promote grain refinement and the formation of nanocrystalline domains, thereby improving both strength and ductility. Enhanced effects have been observed in Cu–Cr–Zr and Cu–Al–Ni alloys, particularly when prestrain is introduced via cold rolling or friction stir processing (FSP). In these systems, microstructural stability during post-deformation ageing is markedly improved due to the suppression of grain coarsening and the controlled precipitation of strengthening phases. Moreover, prestrain modifies the local chemical and crystallographic environment in a manner that critically impacts electrochemical behavior. Intermediate levels of mechanical stress have been shown to improve corrosion resistance by facilitating the formation of uniform, adherent passive films, while excessive strain introduces microstructural heterogeneities that serve as initiation sites for intergranular and stress corrosion cracking. These phenomena were characterized using X-ray diffraction, scanning and transmission electron microscopy (TEM), and electrochemical techniques including potentiodynamic polarization and electrochemical impedance spectroscopy. The interplay between mechanical preconditioning, microstructural refinement, and corrosion mechanisms has been clarified, offering insights into process–structure–property relationships. The findings hold particular relevance for the design and optimization of copper alloys in high-performance applications such as electronic interconnects, biomedical implants, and aerospace components, where dimensional stability, chemical resilience, and machinability are of paramount importance. The study underscores the critical role of prestrain not only as a structural refinement tool but also as a means of tailoring corrosion resistance through controlled microstructural engineering. ]]> Influence of Prestrain on Microstructural Evolution and Corrosion Behavior of Copper-Based Alloys muhssn hamzah shamky haider zghair jumaah talib ali ridha elias noaman abdulrahman karam doi: 10.56578/pmdf020204 Precision Mechanics & Digital Fabrication 06-29-2025 Precision Mechanics & Digital Fabrication 06-29-2025 2025 2 2 Article 108 10.56578/pmdf020204 https://www.acadlore.com/article/PMDF/2025_2_2/pmdf020204 https://www.acadlore.com/article/PMDF/2025_2_2/pmdf020203 The widespread adoption of electric vehicles (EVs) has brought about critical challenges in brake rotor performance, primarily attributed to the reduced reliance on conventional friction braking systems. This decreased usage, owing to the predominant application of regenerative braking, has inadvertently increased the susceptibility of brake rotors—particularly those manufactured from grey cast iron (GCI)—to corrosion and non-traditional wear mechanisms due to extended exposure to environmental elements. These challenges are compounded by the global imperative for sustainable transportation solutions, as emphasized in the European Union (EU)’s roadmap for climate-neutral mobility. In this context, the development and implementation of sustainable strategies to improve the wear and corrosion resistance of EV brake rotors have become paramount. This review synthesizes recent advancements in environmentally conscious approaches, including the application of eco-friendly surface treatments, alloying modifications, microstructural engineering, and solid or dry lubrication techniques tailored for GCI rotors. The analysis extends to the evaluation of scalability, cost-efficiency, tribological stability, and environmental compatibility over the rotors' service life. Particular attention is devoted to emergent solutions such as bio-inspired multifunctional coatings, integration of intelligent condition-monitoring technologies, and rotor design optimized through data-driven predictive modelling. The necessity for robust life cycle assessments (LCA) is underscored, aiming to holistically quantify environmental impact from raw material extraction through end-of-life disposal or recycling. Key research gaps are identified, including the limited real-world validation of novel materials under EV-specific load profiles and insufficient understanding of synergistic degradation modes under mixed braking regimes. It is suggested that a multidisciplinary research agenda—merging materials science, tribology, electrochemistry, and intelligent systems—is essential to advance the next generation of high-performance, low-impact braking solutions. In doing so, a comprehensive framework for sustainable brake rotor innovation in EVs can be established, aligning material resilience with broader environmental and regulatory goals. 06-29-2025 <![CDATA[ <p>The widespread adoption of electric vehicles (EVs) has brought about critical challenges in brake rotor performance, primarily attributed to the reduced reliance on conventional friction braking systems. This decreased usage, owing to the predominant application of regenerative braking, has inadvertently increased the susceptibility of brake rotors—particularly those manufactured from grey cast iron (GCI)—to corrosion and non-traditional wear mechanisms due to extended exposure to environmental elements. These challenges are compounded by the global imperative for sustainable transportation solutions, as emphasized in the European Union (EU)’s roadmap for climate-neutral mobility. In this context, the development and implementation of sustainable strategies to improve the wear and corrosion resistance of EV brake rotors have become paramount. This review synthesizes recent advancements in environmentally conscious approaches, including the application of eco-friendly surface treatments, alloying modifications, microstructural engineering, and solid or dry lubrication techniques tailored for GCI rotors. The analysis extends to the evaluation of scalability, cost-efficiency, tribological stability, and environmental compatibility over the rotors' service life. Particular attention is devoted to emergent solutions such as bio-inspired multifunctional coatings, integration of intelligent condition-monitoring technologies, and rotor design optimized through data-driven predictive modelling. The necessity for robust life cycle assessments (LCA) is underscored, aiming to holistically quantify environmental impact from raw material extraction through end-of-life disposal or recycling. Key research gaps are identified, including the limited real-world validation of novel materials under EV-specific load profiles and insufficient understanding of synergistic degradation modes under mixed braking regimes. It is suggested that a multidisciplinary research agenda—merging materials science, tribology, electrochemistry, and intelligent systems—is essential to advance the next generation of high-performance, low-impact braking solutions. In doing so, a comprehensive framework for sustainable brake rotor innovation in EVs can be established, aligning material resilience with broader environmental and regulatory goals.</p> ]]> Sustainable Enhancement of Brake Rotor Durability in Electric Vehicles: Challenges, Innovations, and Future Directions samuel a. awe doi: 10.56578/pmdf020203 Precision Mechanics & Digital Fabrication 06-29-2025 Precision Mechanics & Digital Fabrication 06-29-2025 2025 2 2 Article 93 10.56578/pmdf020203 https://www.acadlore.com/article/PMDF/2025_2_2/pmdf020203 https://www.acadlore.com/article/PMDF/2025_2_2/pmdf020202 The effectiveness of single-axis solar tracking in enhancing the performance of flat-plate solar collectors (FPSCs) has been widely acknowledged, particularly under clear-sky conditions. However, the precision of solar tracking systems—governed by the electro-mechanical transmission's discrete rotation step size—has a critical impact on energy yield. In this study, the influence of varying rotation step sizes on the incident solar irradiance received by flat-plate collectors with single-axis tracking (SAT) has been numerically investigated using the EnergyPlus simulation environment. Eight discrete step sizes—1°, 2°, 5°, 10°, 15°, 30°, 45°, and 90°—were examined under clear-sky conditions on July 26, using meteorological data specific to Kragujevac, Serbia. The tracking system was configured to follow the solar trajectory along the east–west (E–W) direction, rotating around a north–south (N–S) inclined axis. Results demonstrated that incident solar irradiance was significantly enhanced—by over 35%—when rotation step sizes ranged between 1° and 15°, compared to fixed (non-tracking) collectors. Slight reductions in performance were observed for step sizes of 30° (34.26% improvement) and 45° (32.95%), with the lowest gain (23.04%) associated with the coarsest resolution of 90°. Although dual-axis tracking (DAT) systems provide superior irradiance capture, single-axis systems offer substantial advantages in residential and small-scale applications due to their lower capital investment, simpler design, reduced maintenance requirements, and greater architectural integration potential. These findings underscore the importance of optimizing rotation step size in the design and deployment of cost-effective, energy-efficient solar tracking systems. In light of increasingly stringent energy performance directives within the European Union, the deployment of optimally configured SAT systems is expected to expand across the residential sector. 05-26-2025 <![CDATA[ <p>The effectiveness of single-axis solar tracking in enhancing the performance of flat-plate solar collectors (FPSCs) has been widely acknowledged, particularly under clear-sky conditions. However, the precision of solar tracking systems—governed by the electro-mechanical transmission's discrete rotation step size—has a critical impact on energy yield. In this study, the influence of varying rotation step sizes on the incident solar irradiance received by flat-plate collectors with single-axis tracking (SAT) has been numerically investigated using the EnergyPlus simulation environment. Eight discrete step sizes—1°, 2°, 5°, 10°, 15°, 30°, 45°, and 90°—were examined under clear-sky conditions on July 26, using meteorological data specific to Kragujevac, Serbia. The tracking system was configured to follow the solar trajectory along the east–west (E–W) direction, rotating around a north–south (N–S) inclined axis. Results demonstrated that incident solar irradiance was significantly enhanced—by over 35%—when rotation step sizes ranged between 1° and 15°, compared to fixed (non-tracking) collectors. Slight reductions in performance were observed for step sizes of 30° (34.26% improvement) and 45° (32.95%), with the lowest gain (23.04%) associated with the coarsest resolution of 90°. Although dual-axis tracking (DAT) systems provide superior irradiance capture, single-axis systems offer substantial advantages in residential and small-scale applications due to their lower capital investment, simpler design, reduced maintenance requirements, and greater architectural integration potential. These findings underscore the importance of optimizing rotation step size in the design and deployment of cost-effective, energy-efficient solar tracking systems. In light of increasingly stringent energy performance directives within the European Union, the deployment of optimally configured SAT systems is expected to expand across the residential sector.</p> ]]> Influence of Rotation Step Size on Incident Solar Irradiance in Flat-Plate Collectors with Single-Axis Tracking Under Clear-Sky Conditions aleksandar nešović igor saveljić doi: 10.56578/pmdf020202 Precision Mechanics & Digital Fabrication 05-26-2025 Precision Mechanics & Digital Fabrication 05-26-2025 2025 2 2 Article 83 10.56578/pmdf020202 https://www.acadlore.com/article/PMDF/2025_2_2/pmdf020202 https://www.acadlore.com/article/PMDF/2025_2_2/pmdf020201 Curved multi-layer beams, such as leaf springs, are widely used in vehicle suspension systems for both road and rail vehicles in automotive industry due to their capacity for high loads and their vibrational damping properties. To design suspension systems that experience a large number of load types and complexities of friction, we must first understand the nonlinear dynamic behavior of curved beams. In this paper, the governing equations for the nonlinear vibrations of curved two-layer beams in the presence of interlayer slip are first derived. Then, the characteristic equation, the longitudinal and transverse mode shapes of the beam, are determined independently using eigenvalue problem solutions. Subsequently, using the calculated mode shapes, different phases of the dynamics of these structures are investigated, taking into account interlayer friction. The results of numerical simulations are compared and validated with finite element analysis using ANSYS software. The results show that the dynamic behavior of curved two-layer beams experiences chaotic regimes after initial slip. Different regimes of periodic, quasi-periodic and chaotic motions are found in the dynamics of the system. 04-26-2025 <![CDATA[ <p>Curved multi-layer beams, such as leaf springs, are widely used in vehicle suspension systems for both road and rail vehicles in automotive industry due to their capacity for high loads and their vibrational damping properties. To design suspension systems that experience a large number of load types and complexities of friction, we must first understand the nonlinear dynamic behavior of curved beams. In this paper, the governing equations for the nonlinear vibrations of curved two-layer beams in the presence of interlayer slip are first derived. Then, the characteristic equation, the longitudinal and transverse mode shapes of the beam, are determined independently using eigenvalue problem solutions. Subsequently, using the calculated mode shapes, different phases of the dynamics of these structures are investigated, taking into account interlayer friction. The results of numerical simulations are compared and validated with finite element analysis using ANSYS software. The results show that the dynamic behavior of curved two-layer beams experiences chaotic regimes after initial slip. Different regimes of periodic, quasi-periodic and chaotic motions are found in the dynamics of the system.</p> ]]> Nonlinear Vibrations and Chaotic Behavior of Curved Two-Layer Beam-Like Structures Used in Automotive Industry hamid m. sedighi koroush h. shirazi khosro naderan tahan doi: 10.56578/pmdf020201 Precision Mechanics & Digital Fabrication 04-26-2025 Precision Mechanics & Digital Fabrication 04-26-2025 2025 2 2 Article 67 10.56578/pmdf020201 https://www.acadlore.com/article/PMDF/2025_2_2/pmdf020201 https://www.acadlore.com/article/PMDF/2025_2_1/pmdf020105 The accurate determination of the conduit water starting time constant ($T_w$) is critical for optimizing hydro turbine performance and dynamic control in hydropower plants. Instead of relying on conventional calculation methods, machine learning (ML) techniques, specifically long short-term memory (LSTM) networks and multilayer perceptron (MLP) models, have been employed to identify $T_w$. The dataset used for model training and validation comprises real operational data collected from two hydropower plants. The effectiveness of both algorithms in $T_w$ identification has been evaluated through simulation, with Python serving as the primary programming environment. The findings indicate that, despite its more complex architecture, LSTM does not necessarily yield superior results. In contrast, MLP, as a relatively simpler model, demonstrates greater accuracy in estimating $T_w$, suggesting that intricate network structures are not always required for precise identification. Additionally, an optimization function ($F_\text{opt}$) has been utilized to assess the reliability of the identified $T_w$ values by comparing them with actual hydro turbine responses. The results underscore the practicality of MLP in hydropower system modeling, providing a computationally efficient alternative for conduit water starting time constant identification. These insights contribute to improving real-time turbine control and enhancing the efficiency of hydropower generation. 03-30-2025 <![CDATA[ <p>The accurate determination of the conduit water starting time constant ($T_w$) is critical for optimizing hydro turbine performance and dynamic control in hydropower plants. Instead of relying on conventional calculation methods, machine learning (ML) techniques, specifically long short-term memory (LSTM) networks and multilayer perceptron (MLP) models, have been employed to identify $T_w$. The dataset used for model training and validation comprises real operational data collected from two hydropower plants. The effectiveness of both algorithms in $T_w$ identification has been evaluated through simulation, with Python serving as the primary programming environment. The findings indicate that, despite its more complex architecture, LSTM does not necessarily yield superior results. In contrast, MLP, as a relatively simpler model, demonstrates greater accuracy in estimating $T_w$, suggesting that intricate network structures are not always required for precise identification. Additionally, an optimization function ($F_\text{opt}$) has been utilized to assess the reliability of the identified $T_w$ values by comparing them with actual hydro turbine responses. The results underscore the practicality of MLP in hydropower system modeling, providing a computationally efficient alternative for conduit water starting time constant identification. These insights contribute to improving real-time turbine control and enhancing the efficiency of hydropower generation.</p> ]]> Identification of the Conduit Water Starting Time Constant in Hydropower Plants Using LSTM and MLP Machine Learning Algorithms radmila koleva darko babunski emil zaev doi: 10.56578/pmdf020105 Precision Mechanics & Digital Fabrication 03-30-2025 Precision Mechanics & Digital Fabrication 03-30-2025 2025 2 1 Article 57 10.56578/pmdf020105 https://www.acadlore.com/article/PMDF/2025_2_1/pmdf020105 https://www.acadlore.com/article/PMDF/2025_2_1/pmdf020104 This paper investigates the kinematic solution of cable-driven hyper-redundant manipulators, focusing on the transformation from the cable-driven space to the joint space. Two forward kinematics solution networks based on residual networks and bidirectional long short-term memory (BiLSTM) networks are proposed and compared. First, a single-joint kinematic model is established based on the topology of the cable-driven hyper-redundant manipulator, providing the mapping relationship between cable length variations and joint angles. The decoupling problem between the cable-driven space and joint space is analyzed, extending the decoupling method from a two-joint scenario to a multi-joint scenario, leading to the derivation of coupled equations between cable lengths and joint angles. Subsequently, both a single-joint forward kinematics solution network and a multi-joint forward kinematics solution network are designed and trained separately. Finally, their performance is evaluated using a test dataset. The results demonstrate that the multi-joint forward kinematics solution network significantly outperforms the single-joint network in terms of both accuracy and computational efficiency. 03-11-2025 <![CDATA[ <p>This paper investigates the kinematic solution of cable-driven hyper-redundant manipulators, focusing on the transformation from the cable-driven space to the joint space. Two forward kinematics solution networks based on residual networks and bidirectional long short-term memory (BiLSTM) networks are proposed and compared. First, a single-joint kinematic model is established based on the topology of the cable-driven hyper-redundant manipulator, providing the mapping relationship between cable length variations and joint angles. The decoupling problem between the cable-driven space and joint space is analyzed, extending the decoupling method from a two-joint scenario to a multi-joint scenario, leading to the derivation of coupled equations between cable lengths and joint angles. Subsequently, both a single-joint forward kinematics solution network and a multi-joint forward kinematics solution network are designed and trained separately. Finally, their performance is evaluated using a test dataset. The results demonstrate that the multi-joint forward kinematics solution network significantly outperforms the single-joint network in terms of both accuracy and computational efficiency.</p> ]]> Forward Kinematics Solution for Cable-Driven Hyper-Redundant Manipulators Based on BiLSTM tianao wang zhenghao liang guolei wang doi: 10.56578/pmdf020104 Precision Mechanics & Digital Fabrication 03-11-2025 Precision Mechanics & Digital Fabrication 03-11-2025 2025 2 1 Article 44 10.56578/pmdf020104 https://www.acadlore.com/article/PMDF/2025_2_1/pmdf020104 https://www.acadlore.com/article/PMDF/2025_2_1/pmdf020103 Rolling bearings, as key components of rotating machinery, play a crucial role in the reliable operation of equipment. Over time, rolling bearings inevitably experience wear and fatigue, leading to damage. Accurate prediction of their Remaining Useful Life (RUL) is of paramount importance. This paper proposes an RUL prediction model based on the Multi-Scale Temporal Convolutional Network (MSTCN). The model effectively integrates both time-domain and frequency-domain information from bearing vibration signals through a multi-scale feature extraction module, enabling it to capture feature representations at different time scales. Additionally, the MSTCN's powerful temporal modeling capabilities allow it to capture long-term dependencies and short-term fluctuations in the bearing degradation process. Experimental results show that, compared to traditional methods, the proposed MSTCN model significantly improves the accuracy and stability of RUL predictions on the PHM2012 bearing dataset, demonstrating the effectiveness of the method in predicting the RUL of rolling bearings. 03-06-2025 <![CDATA[ <p>Rolling bearings, as key components of rotating machinery, play a crucial role in the reliable operation of equipment. Over time, rolling bearings inevitably experience wear and fatigue, leading to damage. Accurate prediction of their Remaining Useful Life (RUL) is of paramount importance. This paper proposes an RUL prediction model based on the Multi-Scale Temporal Convolutional Network (MSTCN). The model effectively integrates both time-domain and frequency-domain information from bearing vibration signals through a multi-scale feature extraction module, enabling it to capture feature representations at different time scales. Additionally, the MSTCN's powerful temporal modeling capabilities allow it to capture long-term dependencies and short-term fluctuations in the bearing degradation process. Experimental results show that, compared to traditional methods, the proposed MSTCN model significantly improves the accuracy and stability of RUL predictions on the PHM2012 bearing dataset, demonstrating the effectiveness of the method in predicting the RUL of rolling bearings.</p> ]]> A Multi-Scale Temporal Convolutional Network Approach for Remaining Useful Life Prediction of Rolling Bearings tichun wang qiji teng doi: 10.56578/pmdf020103 Precision Mechanics & Digital Fabrication 03-06-2025 Precision Mechanics & Digital Fabrication 03-06-2025 2025 2 1 Article 31 10.56578/pmdf020103 https://www.acadlore.com/article/PMDF/2025_2_1/pmdf020103 https://www.acadlore.com/article/PMDF/2025_2_1/pmdf020102 This study investigates the structural response of a hub steering knuckle from a Toyota Camry LE under typical in-service loading conditions, with a focus on material performance comparison. Aluminium alloy A356.0-T6 and grey cast iron were selected as candidate materials for the analysis. A three-dimensional (3D) model of the hub steering knuckle was generated using SolidWorks 2018, while static structural simulations were conducted with ANSYS Workbench R15.0 (2019 version). The factor of safety (FOS) was varied between 2.293 and 15 to account for the diverse operational scenarios. The applied loading conditions were derived from the cumulative forces acting on the four tyres of the vehicle, with a total force of 3938.715 N in the Z-direction. The steering moment was calculated to be 5400 N·mm at a perpendicular distance of 108 mm, while the braking force amounted to 3964.63 N·mm, with a corresponding braking moment of 277,524.73 N·mm, all determined using standard analytical formulas. A solid mesh type was employed for the finite element analysis (FEA), with a blended curvature-based meshing technique applied. The results of the analysis showed that, for A356.0-T6, the maximum equivalent Von Mises stress (VMS), maximum equivalent elastic strain, maximum principal stress, and maximum shear stress were 36.079 MPa, 0.00018393 mm/mm, 44.587 MPa, and 19.871 MPa, respectively. In comparison, grey cast iron exhibited values of 24.016 MPa, 0.00013104 mm/mm, 41.214 MPa, and 18.625 MPa, respectively. The maximum directional deformations along the Z-axis for A356.0-T6 and grey cast iron were 0.010135 mm and 0.007275 mm, respectively. The maximum total deformations were recorded at 0.069036 mm and 0.048725 mm for A356.0-T6 and grey cast iron, respectively. These findings suggest that both materials are suitable for use in hub steering knuckles, with grey cast iron being preferable when impact resistance is a priority, whereas A356.0-T6 is more suitable for applications requiring lightweight and corrosion resistance. The results contribute to the understanding of material selection for automotive components, considering both mechanical performance and operational demands. 02-16-2025 <![CDATA[ <p>This study investigates the structural response of a hub steering knuckle from a Toyota Camry LE under typical in-service loading conditions, with a focus on material performance comparison. Aluminium alloy A356.0-T6 and grey cast iron were selected as candidate materials for the analysis. A three-dimensional (3D) model of the hub steering knuckle was generated using SolidWorks 2018, while static structural simulations were conducted with ANSYS Workbench R15.0 (2019 version). The factor of safety (FOS) was varied between 2.293 and 15 to account for the diverse operational scenarios. The applied loading conditions were derived from the cumulative forces acting on the four tyres of the vehicle, with a total force of 3938.715 N in the Z-direction. The steering moment was calculated to be 5400 N·mm at a perpendicular distance of 108 mm, while the braking force amounted to 3964.63 N·mm, with a corresponding braking moment of 277,524.73 N·mm, all determined using standard analytical formulas. A solid mesh type was employed for the finite element analysis (FEA), with a blended curvature-based meshing technique applied. The results of the analysis showed that, for A356.0-T6, the maximum equivalent Von Mises stress (VMS), maximum equivalent elastic strain, maximum principal stress, and maximum shear stress were 36.079 MPa, 0.00018393 mm/mm, 44.587 MPa, and 19.871 MPa, respectively. In comparison, grey cast iron exhibited values of 24.016 MPa, 0.00013104 mm/mm, 41.214 MPa, and 18.625 MPa, respectively. The maximum directional deformations along the Z-axis for A356.0-T6 and grey cast iron were 0.010135 mm and 0.007275 mm, respectively. The maximum total deformations were recorded at 0.069036 mm and 0.048725 mm for A356.0-T6 and grey cast iron, respectively. These findings suggest that both materials are suitable for use in hub steering knuckles, with grey cast iron being preferable when impact resistance is a priority, whereas A356.0-T6 is more suitable for applications requiring lightweight and corrosion resistance. The results contribute to the understanding of material selection for automotive components, considering both mechanical performance and operational demands.</p> ]]> Finite Element Analysis of In-Service Loading on Hub Steering Knuckles: A Comparison of A356.0-T6 and Grey Cast Iron aniekan essienubong ikpe jephtar uviefovwe ohwoekevwo imoh ime ekanem doi: 10.56578/pmdf020102 Precision Mechanics & Digital Fabrication 02-16-2025 Precision Mechanics & Digital Fabrication 02-16-2025 2025 2 1 Article 12 10.56578/pmdf020102 https://www.acadlore.com/article/PMDF/2025_2_1/pmdf020102 https://www.acadlore.com/article/PMDF/2025_2_1/pmdf020101 In the ship hull plate welding process, different welding sequences directly affect the deformation of the current welding procedure, which in turn impacts the overall shipbuilding accuracy. This study takes a typical double T-shaped thin plate structure as an example. Based on welding numerical simulation and experimental validation, a corresponding dataset is obtained. To address the issue of BP neural networks being prone to local optima, which can lead to inaccurate results, a Simulated Annealing-Back Propagation (SA-BP) neural network model is used to analyze the dataset. The research aims to determine the optimal welding sequence that minimizes deformation. The training results show that the Mean Squared Error (MSE) of the SA-BP model decreased from 1.0144 in the BP model to 0.67388. Additionally, the SA-BP model's fitting performance is far superior to that of the BP model. Therefore, the SA-BP neural network model provides more stable and accurate results compared to the traditional BP neural network model. The comparison of the optimal welding sequence results derived from both models shows that welding with the optimized SA-BP neural network results in a 21.07% reduction in welding deformation compared to the traditional BP neural network. 01-19-2025 <![CDATA[ <p>In the ship hull plate welding process, different welding sequences directly affect the deformation of the current welding procedure, which in turn impacts the overall shipbuilding accuracy. This study takes a typical double T-shaped thin plate structure as an example. Based on welding numerical simulation and experimental validation, a corresponding dataset is obtained. To address the issue of BP neural networks being prone to local optima, which can lead to inaccurate results, a Simulated Annealing-Back Propagation (SA-BP) neural network model is used to analyze the dataset. The research aims to determine the optimal welding sequence that minimizes deformation. The training results show that the Mean Squared Error (MSE) of the SA-BP model decreased from 1.0144 in the BP model to 0.67388. Additionally, the SA-BP model's fitting performance is far superior to that of the BP model. Therefore, the SA-BP neural network model provides more stable and accurate results compared to the traditional BP neural network model. The comparison of the optimal welding sequence results derived from both models shows that welding with the optimized SA-BP neural network results in a 21.07% reduction in welding deformation compared to the traditional BP neural network.</p> ]]> Optimization of Hull Thin Plate Welding Sequence Based on Simulated Annealing-Back Propagation Neural Network hao xu daofang chang chiyike zhang doi: 10.56578/pmdf020101 Precision Mechanics & Digital Fabrication 01-19-2025 Precision Mechanics & Digital Fabrication 01-19-2025 2025 2 1 Article 1 10.56578/pmdf020101 https://www.acadlore.com/article/PMDF/2025_2_1/pmdf020101 https://www.acadlore.com/article/PMDF/2024_1_4/pmdf010405 A novel composite mechanical bulging process suitable for the manufacture of medium-duty commercial vehicle drive axle housings is proposed. The analytical expression for the limit bulging forming coefficient of tube blanks under conditions below the metal recrystallization temperature is derived, and the influence of the matching of various force parameters on the limit bulging forming coefficient is analyzed. The appropriate range for the axial auxiliary load during radial bulging is also presented. Based on the derived theory, a 5-ton commercial vehicle drive axle housing is selected as the research object. The key processes in the forming process are numerically simulated to obtain the metal flow state, stress-strain distribution, and wall thickness variation. The types and locations of defects that may occur during the bulging process are also predicted. To address the phenomenon of local wall thinning in the composite mechanical bulging process of the drive axle housing, a set of orthogonal simulation experiments is designed, focusing on the wall thickness thinning rate in the bridge arch bulging area and the crack-prone region, with respect to the process parameters. Based on the numerical simulation results, response surface equations are established for the expansion core's movement speed and axial auxiliary thrust in relation to the wall thickness thinning rate. Through parameter estimation of the response surface equation and regression analysis of significant influencing factors, the effects of process parameters on wall thickness thinning are obtained: the thinning rate in the bridge arch bulging area decreases with increasing expansion core movement speed and axial auxiliary thrust, while the thinning rate in the crack-prone region increases. The optimization of the response surface model and the determination of the optimal process parameter combination, based on field production conditions, show that the numerical simulation results and the wall thickness measurements from process experiments are in close agreement. No cracks occur in the axle housing, and the thinning is effectively alleviated. In contrast, mechanical bulging without axial auxiliary thrust leads to cracks, thus validating the feasibility of the proposed process scheme and the effectiveness of the parameter optimization. This research provides valuable technical reference for upgrading the manufacturing technology of large-span axle-tube products. 12-30-2024 <![CDATA[ <p>A novel composite mechanical bulging process suitable for the manufacture of medium-duty commercial vehicle drive axle housings is proposed. The analytical expression for the limit bulging forming coefficient of tube blanks under conditions below the metal recrystallization temperature is derived, and the influence of the matching of various force parameters on the limit bulging forming coefficient is analyzed. The appropriate range for the axial auxiliary load during radial bulging is also presented. Based on the derived theory, a 5-ton commercial vehicle drive axle housing is selected as the research object. The key processes in the forming process are numerically simulated to obtain the metal flow state, stress-strain distribution, and wall thickness variation. The types and locations of defects that may occur during the bulging process are also predicted. To address the phenomenon of local wall thinning in the composite mechanical bulging process of the drive axle housing, a set of orthogonal simulation experiments is designed, focusing on the wall thickness thinning rate in the bridge arch bulging area and the crack-prone region, with respect to the process parameters. Based on the numerical simulation results, response surface equations are established for the expansion core's movement speed and axial auxiliary thrust in relation to the wall thickness thinning rate. Through parameter estimation of the response surface equation and regression analysis of significant influencing factors, the effects of process parameters on wall thickness thinning are obtained: the thinning rate in the bridge arch bulging area decreases with increasing expansion core movement speed and axial auxiliary thrust, while the thinning rate in the crack-prone region increases. The optimization of the response surface model and the determination of the optimal process parameter combination, based on field production conditions, show that the numerical simulation results and the wall thickness measurements from process experiments are in close agreement. No cracks occur in the axle housing, and the thinning is effectively alleviated. In contrast, mechanical bulging without axial auxiliary thrust leads to cracks, thus validating the feasibility of the proposed process scheme and the effectiveness of the parameter optimization. This research provides valuable technical reference for upgrading the manufacturing technology of large-span axle-tube products.</p> ]]> Analysis and Experimental Study of the Composite Mechanical Bulging Process for Medium-Duty Commercial Vehicle Drive Axle Housing pan li jitao zhou xuexu yuan junwei zhao xiaowei fu jian zeng doi: 10.56578/pmdf010405 Precision Mechanics & Digital Fabrication 12-30-2024 Precision Mechanics & Digital Fabrication 12-30-2024 2024 1 4 Article 235 10.56578/pmdf010405 https://www.acadlore.com/article/PMDF/2024_1_4/pmdf010405 https://www.acadlore.com/article/PMDF/2024_1_4/pmdf010404 Incremental sheet metal forming (ISMF) is a promising manufacturing technique that has gained significant attention due to its ability to produce complex geometries and high-quality products, particularly for small-scale production and rapid prototyping. The integration of industrial robots into the ISMF process, referred to as roboforming, has enabled advancements in this field. However, the inherent limitations of industrial robots—particularly the reduced rigidity of robotic arms with rotary joints—can lead to dimensional inaccuracies and deviations in the final product. These limitations are primarily due to the lack of precise force control during the forming process. To address these challenges, this study introduces a novel approach to roboforming that incorporates force control alongside the position control of the industrial robot. The contact force between the tool and the workpiece is considered as an additional variable in the control loop, with the objective of improving dimensional accuracy and the overall quality of the formed product. A regression analysis was conducted to determine the mean process force required for conical geometries, with the starting radius, infeed depth, wall angle, and supporting angle serving as input variables. Experimental validation revealed that force-controlled incremental forming with a constant contact force is unfeasible, as the pressure force is highly dependent on the current radius of the workpiece and varies during the forming process. Therefore, a new control strategy is proposed, which involves the dynamic adjustment of the contact force, using the variable pressure force as an input parameter. This approach is expected to significantly enhance the precision and reliability of robot-assisted ISMF, offering a pathway for overcoming current limitations in industrial applications. 12-27-2024 <![CDATA[ <p>Incremental sheet metal forming (ISMF) is a promising manufacturing technique that has gained significant attention due to its ability to produce complex geometries and high-quality products, particularly for small-scale production and rapid prototyping. The integration of industrial robots into the ISMF process, referred to as roboforming, has enabled advancements in this field. However, the inherent limitations of industrial robots—particularly the reduced rigidity of robotic arms with rotary joints—can lead to dimensional inaccuracies and deviations in the final product. These limitations are primarily due to the lack of precise force control during the forming process. To address these challenges, this study introduces a novel approach to roboforming that incorporates force control alongside the position control of the industrial robot. The contact force between the tool and the workpiece is considered as an additional variable in the control loop, with the objective of improving dimensional accuracy and the overall quality of the formed product. A regression analysis was conducted to determine the mean process force required for conical geometries, with the starting radius, infeed depth, wall angle, and supporting angle serving as input variables. Experimental validation revealed that force-controlled incremental forming with a constant contact force is unfeasible, as the pressure force is highly dependent on the current radius of the workpiece and varies during the forming process. Therefore, a new control strategy is proposed, which involves the dynamic adjustment of the contact force, using the variable pressure force as an input parameter. This approach is expected to significantly enhance the precision and reliability of robot-assisted ISMF, offering a pathway for overcoming current limitations in industrial applications.</p> ]]> Force-Controlled Path Planning for Robot-Assisted Incremental Sheet Metal Forming: A New Approach to Addressing Dimensional Accuracy Challenges malik čabaravdić dennis möllensiep bernd kuhlenkötter alfred hypki doi: 10.56578/pmdf010404 Precision Mechanics & Digital Fabrication 12-27-2024 Precision Mechanics & Digital Fabrication 12-27-2024 2024 1 4 Article 227 10.56578/pmdf010404 https://www.acadlore.com/article/PMDF/2024_1_4/pmdf010404 https://www.acadlore.com/article/PMDF/2024_1_4/pmdf010403 Precision steel is a critical material in modern engineering, particularly in precision mechanics and high-performance construction. In this study, a mathematical model is presented to simulate the vacuum degassing (VD) process employed to reduce the hydrogen content in steel produced via the basic oxygen furnace (BOF) process. The steel, which is subsequently used for ingot casting, requires a significant reduction in hydrogen levels— from 7 ppm to below 1.5 ppm—to meet the stringent quality requirements for high-precision applications. This reduction is achieved through the VD process in combination with argon bottom stirring. The model, developed in collaboration with an industrial project in Bosnia and Herzegovina, is designed to predict the necessary degassing time and the temperature variation during the process. The model accounts for the operational parameters specified by the project sponsor and the constraints of the process. Results indicate that the hydrogen content can be reduced within 8.39 minutes under optimal conditions. Furthermore, for a molten steel starting temperature of 1670℃, the final temperature after degassing is predicted to be 1637℃. The applicability of the model has been validated through practical implementation in a new industrial facility, constructed based on the model’s predictions. This study demonstrates the broader utility of the model in designing and optimizing VD processes for precision steel production, offering significant potential for enhancing steel quality and process efficiency in similar industrial settings. 12-24-2024 <![CDATA[ <p>Precision steel is a critical material in modern engineering, particularly in precision mechanics and high-performance construction. In this study, a mathematical model is presented to simulate the vacuum degassing (VD) process employed to reduce the hydrogen content in steel produced via the basic oxygen furnace (BOF) process. The steel, which is subsequently used for ingot casting, requires a significant reduction in hydrogen levels— from 7 ppm to below 1.5 ppm—to meet the stringent quality requirements for high-precision applications. This reduction is achieved through the VD process in combination with argon bottom stirring. The model, developed in collaboration with an industrial project in Bosnia and Herzegovina, is designed to predict the necessary degassing time and the temperature variation during the process. The model accounts for the operational parameters specified by the project sponsor and the constraints of the process. Results indicate that the hydrogen content can be reduced within 8.39 minutes under optimal conditions. Furthermore, for a molten steel starting temperature of 1670℃, the final temperature after degassing is predicted to be 1637℃. The applicability of the model has been validated through practical implementation in a new industrial facility, constructed based on the model’s predictions. This study demonstrates the broader utility of the model in designing and optimizing VD processes for precision steel production, offering significant potential for enhancing steel quality and process efficiency in similar industrial settings.</p> ]]> Mathematical Modelling of the Vacuum Degassing Process for Hydrogen Removal in Precision Steel Production nenad milijić natalya safronova ivan mihajlović aca jovanović doi: 10.56578/pmdf010403 Precision Mechanics & Digital Fabrication 12-24-2024 Precision Mechanics & Digital Fabrication 12-24-2024 2024 1 4 Article 216 10.56578/pmdf010403 https://www.acadlore.com/article/PMDF/2024_1_4/pmdf010403 https://www.acadlore.com/article/PMDF/2024_1_4/pmdf010402 In response to the complex characteristics of gearbox vibration signals, including high frequency, high dimensionality, non-stationarity, non-linearity, and noise interference, this paper proposes a data processing method based on improved compressed sensing. First, the K-means Singular Value Decomposition (K-SVD) dictionary is used for sparse representation, ensuring good sparsity in the frequency domain. Next, a random convolution kernel measurement matrix is employed in place of the traditional Gaussian random matrix, satisfying the equidistant constraint while enhancing both computational and hardware implementation efficiency. Finally, the Generative Flow (GLOW) model is introduced, incorporating the measurement matrix, dictionary matrix, and sparse coefficient matrix into a unified optimization framework for joint solving. Through reversible mapping and probabilistic distribution modeling, the method effectively addresses noise interference and the challenges posed by complex signal distributions. Experimental results show that, compared with traditional compressed sensing methods, the proposed method offers superior signal reconstruction quality and better noise robustness. 12-19-2024 <![CDATA[ <p>In response to the complex characteristics of gearbox vibration signals, including high frequency, high dimensionality, non-stationarity, non-linearity, and noise interference, this paper proposes a data processing method based on improved compressed sensing. First, the K-means Singular Value Decomposition (K-SVD) dictionary is used for sparse representation, ensuring good sparsity in the frequency domain. Next, a random convolution kernel measurement matrix is employed in place of the traditional Gaussian random matrix, satisfying the equidistant constraint while enhancing both computational and hardware implementation efficiency. Finally, the Generative Flow (GLOW) model is introduced, incorporating the measurement matrix, dictionary matrix, and sparse coefficient matrix into a unified optimization framework for joint solving. Through reversible mapping and probabilistic distribution modeling, the method effectively addresses noise interference and the challenges posed by complex signal distributions. Experimental results show that, compared with traditional compressed sensing methods, the proposed method offers superior signal reconstruction quality and better noise robustness.</p> ]]> A Gearbox Vibration Signal Compressed Sensing Method Based on the Improved GLOW Flow Model tichun wang tian xia doi: 10.56578/pmdf010402 Precision Mechanics & Digital Fabrication 12-19-2024 Precision Mechanics & Digital Fabrication 12-19-2024 2024 1 4 Article 201 10.56578/pmdf010402 https://www.acadlore.com/article/PMDF/2024_1_4/pmdf010402 https://www.acadlore.com/article/PMDF/2024_1_4/pmdf010401 The effective utilisation of equipment is essential for achieving the operational goals within production sectors, particularly in industries involving heavy machinery. Throughout its lifecycle, equipment is exposed to dynamic loads and harsh operational environments, leading to potential failures that may significantly shorten their service life. Therefore, evaluating equipment reliability is crucial for mitigating production losses and ensuring continuous operations. This study presents a comprehensive reliability analysis of underground mining machinery, with a focus on Load-Haul-Dump (LHD) systems, which are key to material handling in mining operations. Reliability assessments are performed using methodologies based on the series configuration of repairable systems. The reliability of each LHD system is quantitatively evaluated by employing a feed-forward back-propagation artificial neural network (ANN) model implemented in MATLAB. This model is designed to predict the optimal responses of each LHD machine under varying operational conditions. The results obtained from the ANN model are compared with the calculated reliability values, demonstrating a high degree of correlation between the predicted and observed outcomes. This strong alignment underscores the potential of ANN-based models in accurately forecasting system reliability. Based on the analysis, recommendations are made to identify the most critical components contributing to the system's unreliability, thereby enabling targeted corrective actions. The findings provide valuable insights for engineers seeking to enhance the performance and operational efficiency of mining machinery through more informed maintenance and operational strategies. 12-11-2024 <![CDATA[ <p>The effective utilisation of equipment is essential for achieving the operational goals within production sectors, particularly in industries involving heavy machinery. Throughout its lifecycle, equipment is exposed to dynamic loads and harsh operational environments, leading to potential failures that may significantly shorten their service life. Therefore, evaluating equipment reliability is crucial for mitigating production losses and ensuring continuous operations. This study presents a comprehensive reliability analysis of underground mining machinery, with a focus on Load-Haul-Dump (LHD) systems, which are key to material handling in mining operations. Reliability assessments are performed using methodologies based on the series configuration of repairable systems. The reliability of each LHD system is quantitatively evaluated by employing a feed-forward back-propagation artificial neural network (ANN) model implemented in MATLAB. This model is designed to predict the optimal responses of each LHD machine under varying operational conditions. The results obtained from the ANN model are compared with the calculated reliability values, demonstrating a high degree of correlation between the predicted and observed outcomes. This strong alignment underscores the potential of ANN-based models in accurately forecasting system reliability. Based on the analysis, recommendations are made to identify the most critical components contributing to the system's unreliability, thereby enabling targeted corrective actions. The findings provide valuable insights for engineers seeking to enhance the performance and operational efficiency of mining machinery through more informed maintenance and operational strategies.</p> ]]> Reliability Analysis of Complex Repairable Systems Using Artificial Neural Networks: A Case Study on Underground Mining Machinery balaraju jakkula govinda raj mandela anup kumar tripathi doi: 10.56578/pmdf010401 Precision Mechanics & Digital Fabrication 12-11-2024 Precision Mechanics & Digital Fabrication 12-11-2024 2024 1 4 Article 189 10.56578/pmdf010401 https://www.acadlore.com/article/PMDF/2024_1_4/pmdf010401 https://www.acadlore.com/article/PMDF/2024_1_3/pmdf010305 The transition of contemporary manufacturing processes from digital to post-digital paradigms within the framework of Industry 5.0 necessitates the integration of both technological advancements and human-centered perspectives. This shift demands a high degree of customization and personalization in production processes, impacting both core and supporting operations. This study investigates the development of a software application designed to facilitate strategic goal-setting in manufacturing Small and Medium-sized Enterprises (SMEs) by leveraging a digitalized Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis. The research focuses on the use of this tool to collect, compare, and rank SWOT factors provided by employees and managers, in order to support data-driven strategic decision-making. The initial phase of the study involved a sample of 520 entrepreneurs and business owners from Poland, Slovakia, the Czech Republic, Hungary, and Serbia, which led to the identification of an extensive list of 83 strengths, 92 weaknesses, 78 opportunities, and 86 threats. These factors were stored in a Google Cloud Database, enabling subsequent comparisons with new data. A further 63 senior decision-makers tested the application by entering their own SWOT factors, comparing them with existing ones in the database, and ranking their significance for strategic planning. The rankings were calculated automatically, with the top-ranked factors forming the basis for further analysis. In the final stage, these rankings were reviewed by five experts from the research consortium, who conducted pairwise comparisons and employed Analytic Hierarchy Process (AHP) analysis to develop a Threats, Opportunities, Weaknesses, and Strengths (TOWS) matrix. This matrix identified potential strategic actions to optimize operations within the investigated region. The findings demonstrate the potential of the software tool to enhance strategic decision-making and improve organizational performance in manufacturing SMEs. The results offer practical insights for decision-makers seeking optimal strategies for operational optimization in their organizations. 09-29-2024 <![CDATA[ <p>The transition of contemporary manufacturing processes from digital to post-digital paradigms within the framework of Industry 5.0 necessitates the integration of both technological advancements and human-centered perspectives. This shift demands a high degree of customization and personalization in production processes, impacting both core and supporting operations. This study investigates the development of a software application designed to facilitate strategic goal-setting in manufacturing Small and Medium-sized Enterprises (SMEs) by leveraging a digitalized Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis. The research focuses on the use of this tool to collect, compare, and rank SWOT factors provided by employees and managers, in order to support data-driven strategic decision-making. The initial phase of the study involved a sample of 520 entrepreneurs and business owners from Poland, Slovakia, the Czech Republic, Hungary, and Serbia, which led to the identification of an extensive list of 83 strengths, 92 weaknesses, 78 opportunities, and 86 threats. These factors were stored in a Google Cloud Database, enabling subsequent comparisons with new data. A further 63 senior decision-makers tested the application by entering their own SWOT factors, comparing them with existing ones in the database, and ranking their significance for strategic planning. The rankings were calculated automatically, with the top-ranked factors forming the basis for further analysis. In the final stage, these rankings were reviewed by five experts from the research consortium, who conducted pairwise comparisons and employed Analytic Hierarchy Process (AHP) analysis to develop a Threats, Opportunities, Weaknesses, and Strengths (TOWS) matrix. This matrix identified potential strategic actions to optimize operations within the investigated region. The findings demonstrate the potential of the software tool to enhance strategic decision-making and improve organizational performance in manufacturing SMEs. The results offer practical insights for decision-makers seeking optimal strategies for operational optimization in their organizations.</p> ]]> Digitalization of Strategic Decision-Making in Manufacturing SMEs: A Quantitative SWOT-TOWS Analysis ivan mihajlović martina perišić vesna spasojević brkić isidora milošević nenad milijić doi: 10.56578/pmdf010305 Precision Mechanics & Digital Fabrication 09-29-2024 Precision Mechanics & Digital Fabrication 09-29-2024 2024 1 3 Article 176 10.56578/pmdf010305 https://www.acadlore.com/article/PMDF/2024_1_3/pmdf010305 https://www.acadlore.com/article/PMDF/2024_1_3/pmdf010304 In the production of high-precision electronic connectors, the proper alignment and insertion quality of pins are critical to ensuring product reliability. Any pin misalignment or deformation can lead to electrical failures in connectors, such as poor contact or pin breakage. To address this issue, this paper conducts a systematic dynamic analysis of the pin insertion mechanism in continuous pin insertion machines, aiming to minimize defects during production and inspection processes. The study first outlines the working principles of continuous pin insertion machines and provides a comprehensive analysis of the pin insertion mechanism, control system, and visual inspection system. By establishing a dynamic model of the pin insertion mechanism, the research uses Matlab for simulation to explore the effects of clearance values, motor speeds, and different materials on the dynamic characteristics of the pin bar. Additionally, a comprehensive test platform was constructed, comprising a feeding module, pinhead, servo worktable, pressure sensor, infrared displacement sensor, and an industrial control computer. The experimental results confirm the accuracy of the simulations and reveal specific trends regarding how clearance values, motor driving speeds, and material selection impact the dynamics of the pin bar. The findings of this study not only enhance the operational stability of continuous pin insertion machines but also provide scientific guidance for quality control and defect prevention in precision connector manufacturing. 09-29-2024 <![CDATA[ <p>In the production of high-precision electronic connectors, the proper alignment and insertion quality of pins are critical to ensuring product reliability. Any pin misalignment or deformation can lead to electrical failures in connectors, such as poor contact or pin breakage. To address this issue, this paper conducts a systematic dynamic analysis of the pin insertion mechanism in continuous pin insertion machines, aiming to minimize defects during production and inspection processes. The study first outlines the working principles of continuous pin insertion machines and provides a comprehensive analysis of the pin insertion mechanism, control system, and visual inspection system. By establishing a dynamic model of the pin insertion mechanism, the research uses Matlab for simulation to explore the effects of clearance values, motor speeds, and different materials on the dynamic characteristics of the pin bar. Additionally, a comprehensive test platform was constructed, comprising a feeding module, pinhead, servo worktable, pressure sensor, infrared displacement sensor, and an industrial control computer. The experimental results confirm the accuracy of the simulations and reveal specific trends regarding how clearance values, motor driving speeds, and material selection impact the dynamics of the pin bar. The findings of this study not only enhance the operational stability of continuous pin insertion machines but also provide scientific guidance for quality control and defect prevention in precision connector manufacturing.</p> ]]> Dynamic Analysis of Continuous Pin Insertion Machines and Their Application in Precision Connector Manufacturing yi du doi: 10.56578/pmdf010304 Precision Mechanics & Digital Fabrication 09-29-2024 Precision Mechanics & Digital Fabrication 09-29-2024 2024 1 3 Article 158 10.56578/pmdf010304 https://www.acadlore.com/article/PMDF/2024_1_3/pmdf010304 https://www.acadlore.com/article/PMDF/2024_1_3/pmdf010303 Rolling bearings play a critical role in various industrial applications. However, the complexity and diversity of data, along with the challenge of selecting the most representative features from a large set and reducing dimensionality to lower computational costs, pose significant challenges for accurately predicting the remaining useful life (RUL) of rolling bearings. To address this, a hybrid model combining the broad learning system (BLS) and multi-scale temporal convolutional network (MsTCN) is proposed for RUL prediction of rolling bearings. The BLS is employed to capture a broad range of features from the full-life signals of rolling bearings, while the MsTCN adaptively extracts multi-scale temporal features, effectively capturing both short-term and long-term dependencies in the bearing’s operational process. Additionally, the fusion and optimization of features extracted by BLS and MsTCN enhance the representational power of the prediction model. Experiments conducted on the PHM2012 bearing dataset demonstrate that the proposed method significantly improves model performance and prediction accuracy. 09-29-2024 <![CDATA[ Rolling bearings play a critical role in various industrial applications. However, the complexity and diversity of data, along with the challenge of selecting the most representative features from a large set and reducing dimensionality to lower computational costs, pose significant challenges for accurately predicting the remaining useful life (RUL) of rolling bearings. To address this, a hybrid model combining the broad learning system (BLS) and multi-scale temporal convolutional network (MsTCN) is proposed for RUL prediction of rolling bearings. The BLS is employed to capture a broad range of features from the full-life signals of rolling bearings, while the MsTCN adaptively extracts multi-scale temporal features, effectively capturing both short-term and long-term dependencies in the bearing’s operational process. Additionally, the fusion and optimization of features extracted by BLS and MsTCN enhance the representational power of the prediction model. Experiments conducted on the PHM2012 bearing dataset demonstrate that the proposed method significantly improves model performance and prediction accuracy. ]]> A Remaining Useful Life Prediction Method for Rolling Bearings Based on Broad Learning System - Multi-Scale Temporal Convolutional Network tichun wang qiji teng guanghu jin doi: 10.56578/pmdf010303 Precision Mechanics & Digital Fabrication 09-29-2024 Precision Mechanics & Digital Fabrication 09-29-2024 2024 1 3 Article 145 10.56578/pmdf010303 https://www.acadlore.com/article/PMDF/2024_1_3/pmdf010303 https://www.acadlore.com/article/PMDF/2024_1_3/pmdf010302 This study investigates the structural performance and mass optimization of traditional walkers by comparing aluminum alloy and polymer matrix composites (PMCs) through advanced finite element analysis (FEA) using the ANSYS simulation platform. The FEA results reveal that peak stress, reaching 251.9 MPa, is concentrated at the front wheel support region, highlighting a critical area prone to structural vulnerability. Special attention is required to address potential mechanical limitations in key zones, such as the rear suspension, to prevent premature failure. Comparative analysis demonstrates that walkers fabricated from carbon-epoxy PMCs offer superior stiffness, reduced weight, and enhanced resistance to deformation compared to aluminum alloy counterparts. Notably, under descent conditions, the maximum elastic strain in the carbon-epoxy walker reaches 0.00399 mm/mm, localized in the front wheel support area, as indicated by the simulation results. These findings underscore the significant role of material selection in improving structural integrity and performance across varying operational conditions. The equivalence of stress and strain energy distributions further substantiates the advantages of composite materials over conventional alloys, suggesting that PMCs enable enhanced durability without compromising weight efficiency. The research emphasizes a human-centred approach, aligning material performance with user needs to develop mobility aids that offer long-term structural reliability. Beyond addressing immediate structural concerns, the findings lay the groundwork for future studies involving optimization algorithms and the exploration of alternative composites for assistive devices. The study provides valuable insights into stress distribution, deformation behaviour, and mechanical response, promoting continuous innovation in the design and development of mobility aids. 09-29-2024 <![CDATA[ <p>This study investigates the structural performance and mass optimization of traditional walkers by comparing aluminum alloy and polymer matrix composites (PMCs) through advanced finite element analysis (FEA) using the ANSYS simulation platform. The FEA results reveal that peak stress, reaching 251.9 MPa, is concentrated at the front wheel support region, highlighting a critical area prone to structural vulnerability. Special attention is required to address potential mechanical limitations in key zones, such as the rear suspension, to prevent premature failure. Comparative analysis demonstrates that walkers fabricated from carbon-epoxy PMCs offer superior stiffness, reduced weight, and enhanced resistance to deformation compared to aluminum alloy counterparts. Notably, under descent conditions, the maximum elastic strain in the carbon-epoxy walker reaches 0.00399 mm/mm, localized in the front wheel support area, as indicated by the simulation results. These findings underscore the significant role of material selection in improving structural integrity and performance across varying operational conditions. The equivalence of stress and strain energy distributions further substantiates the advantages of composite materials over conventional alloys, suggesting that PMCs enable enhanced durability without compromising weight efficiency. The research emphasizes a human-centred approach, aligning material performance with user needs to develop mobility aids that offer long-term structural reliability. Beyond addressing immediate structural concerns, the findings lay the groundwork for future studies involving optimization algorithms and the exploration of alternative composites for assistive devices. The study provides valuable insights into stress distribution, deformation behaviour, and mechanical response, promoting continuous innovation in the design and development of mobility aids.</p> ]]> Structural Analysis and Mass Optimization of Mobility Walkers Using Lightweight Polymer Matrix Composites okta bani bharat kumar humagai doi: 10.56578/pmdf010302 Precision Mechanics & Digital Fabrication 09-29-2024 Precision Mechanics & Digital Fabrication 09-29-2024 2024 1 3 Article 131 10.56578/pmdf010302 https://www.acadlore.com/article/PMDF/2024_1_3/pmdf010302 https://www.acadlore.com/article/PMDF/2024_1_3/pmdf010301 Laser additive manufacturing, a pivotal technology in advanced manufacturing, is extensively applied in the restoration industry. However, its development has been hindered by challenges such as residual stress and excessive grain size during the manufacturing process. The integration of ultrasonic enhancement technology with laser cladding has emerged as a prominent research direction, offering significant improvements in the quality and performance of the cladding layer. This review focuses on two primary approaches: ultrasonic-enhanced synchronous laser cladding and ultrasonic strengthening as a post-processing method. The ultrasonic processes discussed include ultrasonic vibration, ultrasonic rolling, ultrasonic impact, and their composite variants. Each method is evaluated for its ability to modify the microstructure, alleviate defects, and enhance the mechanical properties of the cladding layer. While ultrasonic enhancement during synchronous laser cladding primarily facilitates greater molten pool agitation, post-processing techniques induce severe plastic deformation on the surface of the cladding layer. Both approaches have been shown to reduce residual stress, refine grain structure, and improve surface hardness. The underlying mechanisms governing these improvements, particularly microstructural evolution and grain refinement, are examined in detail. Additionally, the potential advantages and limitations of each ultrasonic introduction method are discussed. Finally, the application prospects and future development trends of ultrasonic-enhanced laser cladding are explored, with particular attention to the role of ultrasonic technology in enhancing the durability, wear resistance, and corrosion resistance of cladding layers. The synergy between ultrasonic techniques and laser cladding promises to expand the potential of additive manufacturing in both industrial and repair applications. 09-29-2024 <![CDATA[ <p>Laser additive manufacturing, a pivotal technology in advanced manufacturing, is extensively applied in the restoration industry. However, its development has been hindered by challenges such as residual stress and excessive grain size during the manufacturing process. The integration of ultrasonic enhancement technology with laser cladding has emerged as a prominent research direction, offering significant improvements in the quality and performance of the cladding layer. This review focuses on two primary approaches: ultrasonic-enhanced synchronous laser cladding and ultrasonic strengthening as a post-processing method. The ultrasonic processes discussed include ultrasonic vibration, ultrasonic rolling, ultrasonic impact, and their composite variants. Each method is evaluated for its ability to modify the microstructure, alleviate defects, and enhance the mechanical properties of the cladding layer. While ultrasonic enhancement during synchronous laser cladding primarily facilitates greater molten pool agitation, post-processing techniques induce severe plastic deformation on the surface of the cladding layer. Both approaches have been shown to reduce residual stress, refine grain structure, and improve surface hardness. The underlying mechanisms governing these improvements, particularly microstructural evolution and grain refinement, are examined in detail. Additionally, the potential advantages and limitations of each ultrasonic introduction method are discussed. Finally, the application prospects and future development trends of ultrasonic-enhanced laser cladding are explored, with particular attention to the role of ultrasonic technology in enhancing the durability, wear resistance, and corrosion resistance of cladding layers. The synergy between ultrasonic techniques and laser cladding promises to expand the potential of additive manufacturing in both industrial and repair applications.</p> ]]> Ultrasonic-Enhanced Laser Cladding: Improving Microstructure and Performance Through Synergistic Processing Techniques xinsheng wang yang zheng zhihai cai xian du jian liu haidou wang doi: 10.56578/pmdf010301 Precision Mechanics & Digital Fabrication 09-29-2024 Precision Mechanics & Digital Fabrication 09-29-2024 2024 1 3 Article 111 10.56578/pmdf010301 https://www.acadlore.com/article/PMDF/2024_1_3/pmdf010301 https://www.acadlore.com/article/PMDF/2024_1_2/pmdf010205 A numerical model of a Gas Metal Arc Welding (GMAW)-based Wire Arc Additive Manufacturing (WAAM) process was developed using the Abaqus software, with validation performed against experimental data from existing literature. The model was employed to investigate the influence of heat input and cooling time on residual stress distribution, with particular focus on longitudinal residual stress. Minimal effect was observed with increasing heat input, whereas cooling time significantly affected stress distribution. The impact of unclamping was also examined. It was determined that for heat inputs of 4000 W and 4500 W, longitudinal residual stress decreased by approximately 10% after unclamping. In contrast, for a heat input of 5000 W, longitudinal residual stress increased by 12% following unclamping. Residual stress was found to accumulate predominantly at the interface between the substrate and the deposition wall. This study provides critical insights into the thermal and mechanical behavior of WAAM processes, contributing to a deeper understanding of stress management and control in additive manufacturing of B91 steel. 06-29-2024 <![CDATA[ <p>A numerical model of a Gas Metal Arc Welding (GMAW)-based Wire Arc Additive Manufacturing (WAAM) process was developed using the Abaqus software, with validation performed against experimental data from existing literature. The model was employed to investigate the influence of heat input and cooling time on residual stress distribution, with particular focus on longitudinal residual stress. Minimal effect was observed with increasing heat input, whereas cooling time significantly affected stress distribution. The impact of unclamping was also examined. It was determined that for heat inputs of 4000 W and 4500 W, longitudinal residual stress decreased by approximately 10% after unclamping. In contrast, for a heat input of 5000 W, longitudinal residual stress increased by 12% following unclamping. Residual stress was found to accumulate predominantly at the interface between the substrate and the deposition wall. This study provides critical insights into the thermal and mechanical behavior of WAAM processes, contributing to a deeper understanding of stress management and control in additive manufacturing of B91 steel.</p> ]]> Numerical Simulation and Analysis of Residual Stress in B91 Steel Deposition Using Wire Arc Additive Manufacturing atilla savaş doi: 10.56578/pmdf010205 Precision Mechanics & Digital Fabrication 06-29-2024 Precision Mechanics & Digital Fabrication 06-29-2024 2024 1 2 Article 102 10.56578/pmdf010205 https://www.acadlore.com/article/PMDF/2024_1_2/pmdf010205 https://www.acadlore.com/article/PMDF/2024_1_2/pmdf010204 A torque-based optical fiber flow sensor has been designed and experimentally tested to assess its potential for fluid flow measurement. The sensor utilizes an optical fiber strength modulation principle to achieve flow detection. Detailed attention is given to the design of the sensor structure, including the sensor probe and fiber bundle probe, and the working principle of the torque-based flow sensor is systematically described. A theoretical model of the sensor is established, considering key parameters such as torque (m), radius (r), sensor joint stiffness (SJ), refractive index (n), and radius of curvature (R), which significantly affect its detection performance. Simulations are conducted to obtain Q-M curves under varying parameter conditions, revealing the relationship between sensor output and fluid flow rate. A gas flow detection experiment is subsequently performed on a custom-built experimental platform to evaluate the sensor’s practical performance. The results indicate that the sensor output decreases monotonically with increasing fluid flow for different parameter settings, demonstrating a good linear response within a specific detection range. It is found that the sensitivity of the sensor is influenced by the selection of critical performance parameters and the characteristics of the fluid being measured. For gas flow detection, the sensor output voltage shows an approximately linear decrease with the increase in gas flow. The comparison between simulation and experimental data confirms that both exhibit similar trends, thereby validating the sensor’s applicability in fluid flow detection. This study highlights the potential of torque-based optical fiber flow sensors for accurate and reliable fluid flow measurements. 06-29-2024 <![CDATA[ <p>A torque-based optical fiber flow sensor has been designed and experimentally tested to assess its potential for fluid flow measurement. The sensor utilizes an optical fiber strength modulation principle to achieve flow detection. Detailed attention is given to the design of the sensor structure, including the sensor probe and fiber bundle probe, and the working principle of the torque-based flow sensor is systematically described. A theoretical model of the sensor is established, considering key parameters such as torque (<em>m</em>), radius (<em>r</em>), sensor joint stiffness (<em>S_J</em>), refractive index (<em>n</em>), and radius of curvature (<em>R</em>), which significantly affect its detection performance. Simulations are conducted to obtain <em>Q</em>-<em>M</em> curves under varying parameter conditions, revealing the relationship between sensor output and fluid flow rate. A gas flow detection experiment is subsequently performed on a custom-built experimental platform to evaluate the sensor’s practical performance. The results indicate that the sensor output decreases monotonically with increasing fluid flow for different parameter settings, demonstrating a good linear response within a specific detection range. It is found that the sensitivity of the sensor is influenced by the selection of critical performance parameters and the characteristics of the fluid being measured. For gas flow detection, the sensor output voltage shows an approximately linear decrease with the increase in gas flow. The comparison between simulation and experimental data confirms that both exhibit similar trends, thereby validating the sensor’s applicability in fluid flow detection. This study highlights the potential of torque-based optical fiber flow sensors for accurate and reliable fluid flow measurements.</p> ]]> Design and Performance Analysis of a Torque-Based Optical Fiber Flow Sensor hao hu liqiong zhong doi: 10.56578/pmdf010204 Precision Mechanics & Digital Fabrication 06-29-2024 Precision Mechanics & Digital Fabrication 06-29-2024 2024 1 2 Article 91 10.56578/pmdf010204 https://www.acadlore.com/article/PMDF/2024_1_2/pmdf010204 https://www.acadlore.com/article/PMDF/2024_1_2/pmdf010203 The aerodynamic and structural performance of aircraft wings constructed from Boron Aluminum Metal Matrix Composites (Boron Al MMC) and conventional aluminum alloys has been comprehensively evaluated through Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI) studies. The CFD analysis was conducted using ANSYS CFX to investigate the aerodynamic behavior, while the FSI analysis was performed using ANSYS Structural to assess the interaction between fluid flow and structural response under various loading conditions. The findings have demonstrated that wings composed of Boron Al MMC exhibit superior performance in terms of strength, stiffness, and durability when compared to aluminum alloys. Under similar aerodynamic loads, the Boron Al MMC material maintained higher structural integrity, demonstrating a 2.28% reduction in equivalent stress, a 30.1% decrease in induced shear stress, a 69.12% reduction in induced deformation, and a 66.35% lower strain energy relative to the aluminum alloy. These results suggest that Boron Al MMC offers enhanced structural stability at high speeds, especially at speeds exceeding Mach 1, as well as under diverse flight conditions involving high G-forces. The significant reduction in deformation and stress concentrations indicates that Boron Al MMC provides improved resilience against damage under high aerodynamic loads. This analysis underlines the potential of Boron Al MMC as a promising material for aircraft wing construction, capable of delivering improved aerodynamic performance, extended service life, and heightened safety margins. Such properties make it a viable alternative to traditional materials, particularly in advanced aerospace applications where strength, stiffness, and durability are critical. The integration of Boron Al MMC could lead to significant advancements in the development of more efficient and reliable aircraft wings. 06-29-2024 <![CDATA[ <p>The aerodynamic and structural performance of aircraft wings constructed from Boron Aluminum Metal Matrix Composites (Boron Al MMC) and conventional aluminum alloys has been comprehensively evaluated through Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI) studies. The CFD analysis was conducted using ANSYS CFX to investigate the aerodynamic behavior, while the FSI analysis was performed using ANSYS Structural to assess the interaction between fluid flow and structural response under various loading conditions. The findings have demonstrated that wings composed of Boron Al MMC exhibit superior performance in terms of strength, stiffness, and durability when compared to aluminum alloys. Under similar aerodynamic loads, the Boron Al MMC material maintained higher structural integrity, demonstrating a 2.28% reduction in equivalent stress, a 30.1% decrease in induced shear stress, a 69.12% reduction in induced deformation, and a 66.35% lower strain energy relative to the aluminum alloy. These results suggest that Boron Al MMC offers enhanced structural stability at high speeds, especially at speeds exceeding Mach 1, as well as under diverse flight conditions involving high G-forces. The significant reduction in deformation and stress concentrations indicates that Boron Al MMC provides improved resilience against damage under high aerodynamic loads. This analysis underlines the potential of Boron Al MMC as a promising material for aircraft wing construction, capable of delivering improved aerodynamic performance, extended service life, and heightened safety margins. Such properties make it a viable alternative to traditional materials, particularly in advanced aerospace applications where strength, stiffness, and durability are critical. The integration of Boron Al MMC could lead to significant advancements in the development of more efficient and reliable aircraft wings.</p> ]]> Comparative Analysis of Aerodynamic and Structural Performance of Aircraft Wings Using Boron Aluminum Metal Matrix Composites and Aluminum Alloys: A CFD and FSI Approach ambika rimal valliyappan david natarajan doi: 10.56578/pmdf010203 Precision Mechanics & Digital Fabrication 06-29-2024 Precision Mechanics & Digital Fabrication 06-29-2024 2024 1 2 Article 75 10.56578/pmdf010203 https://www.acadlore.com/article/PMDF/2024_1_2/pmdf010203 https://www.acadlore.com/article/PMDF/2024_1_2/pmdf010202 The safety and functionality of flexible photovoltaic (PV) racking systems critically depend on understanding the force and deformation behavior of wire ropes. This study establishes mechanical equilibrium equations to derive the deformation curve, maximum displacement, and maximum tension of wire ropes subjected to loading. Analytical dimensionless equations indicate that variations in the orientation of PV modules do not affect the structural stiffness or forces exerted on the wire ropes. Engineering calculations of maximum displacement and tension are compared with results from finite element simulations, revealing less than a 1% discrepancy between the analytical and numerical outcomes. Analysis of characteristic parameter curves in relation to prestress demonstrates that the maximum deflection span ratio decreases as prestress increases, while the maximum tensile stress rises with increasing prestress. The proposed formulas are validated as both accurate and practical, effectively reflecting the changes in wire rope forces with varying prestress levels. This study provides valuable insights for the mechanical analysis and structural design of flexible PV mounting systems, offering a robust reference for future engineering applications. 06-29-2024 <![CDATA[ <p>The safety and functionality of flexible photovoltaic (PV) racking systems critically depend on understanding the force and deformation behavior of wire ropes. This study establishes mechanical equilibrium equations to derive the deformation curve, maximum displacement, and maximum tension of wire ropes subjected to loading. Analytical dimensionless equations indicate that variations in the orientation of PV modules do not affect the structural stiffness or forces exerted on the wire ropes. Engineering calculations of maximum displacement and tension are compared with results from finite element simulations, revealing less than a 1% discrepancy between the analytical and numerical outcomes. Analysis of characteristic parameter curves in relation to prestress demonstrates that the maximum deflection span ratio decreases as prestress increases, while the maximum tensile stress rises with increasing prestress. The proposed formulas are validated as both accurate and practical, effectively reflecting the changes in wire rope forces with varying prestress levels. This study provides valuable insights for the mechanical analysis and structural design of flexible PV mounting systems, offering a robust reference for future engineering applications.</p> ]]> Formulation of Stiffness and Strength Characteristics of Flexible Wire Ropes and Their Application in Photovoltaic Support Structures chuangju zhang leige xu pengshuai liu doi: 10.56578/pmdf010202 Precision Mechanics & Digital Fabrication 06-29-2024 Precision Mechanics & Digital Fabrication 06-29-2024 2024 1 2 Article 66 10.56578/pmdf010202 https://www.acadlore.com/article/PMDF/2024_1_2/pmdf010202 https://www.acadlore.com/article/PMDF/2024_1_2/pmdf010201 Advancements in 3D printing technology have enabled the creation of highly efficient and cost-effective suppressors, offering significant safety benefits for firearm users. Exposure to firearm noise, even in controlled environments such as shooting ranges, poses serious health risks, necessitating improved noise reduction measures. This study explores the potential of 3D printing to produce novel suppressor designs that effectively reduce sound pressure levels in firearms, specifically focusing on their application with a .22 LR caliber rifle. Suppressors capable of reducing sound levels to below 135 dB, making them safe for adult use without hearing protection, were the primary focus. The research was conducted in two phases: initially, optimal suppressor designs were modeled using SolidWorks computational fluid dynamics (CFD), featuring innovations such as perforated baffles, additional expansion chambers, deep and curved expansion chambers, and perforated tubes extending along the suppressor's length. Following the simulation of these designs, live fire testing was conducted in a controlled shooting range environment. The results demonstrated that all tested designs effectively reduced sound pressure to safe levels. However, the suppressor with a conventional baffle layout supplemented by partitioned expansion chambers proved to be the most efficient, particularly when paired with subsonic ammunition. This study highlights the potential of 3D printing technology to revolutionize suppressor design, offering customizable solutions that enhance both user safety and environmental protection. 06-29-2024 <![CDATA[ <p>Advancements in 3D printing technology have enabled the creation of highly efficient and cost-effective suppressors, offering significant safety benefits for firearm users. Exposure to firearm noise, even in controlled environments such as shooting ranges, poses serious health risks, necessitating improved noise reduction measures. This study explores the potential of 3D printing to produce novel suppressor designs that effectively reduce sound pressure levels in firearms, specifically focusing on their application with a .22 LR caliber rifle. Suppressors capable of reducing sound levels to below 135 dB, making them safe for adult use without hearing protection, were the primary focus. The research was conducted in two phases: initially, optimal suppressor designs were modeled using SolidWorks computational fluid dynamics (CFD), featuring innovations such as perforated baffles, additional expansion chambers, deep and curved expansion chambers, and perforated tubes extending along the suppressor's length. Following the simulation of these designs, live fire testing was conducted in a controlled shooting range environment. The results demonstrated that all tested designs effectively reduced sound pressure to safe levels. However, the suppressor with a conventional baffle layout supplemented by partitioned expansion chambers proved to be the most efficient, particularly when paired with subsonic ammunition. This study highlights the potential of 3D printing technology to revolutionize suppressor design, offering customizable solutions that enhance both user safety and environmental protection.</p> ]]> Innovative 3D-Printed Suppressor Designs: Enhancing Safety and Efficiency in Firearm Use vytautas giedraitis artūras kilikevičius doi: 10.56578/pmdf010201 Precision Mechanics & Digital Fabrication 06-29-2024 Precision Mechanics & Digital Fabrication 06-29-2024 2024 1 2 Article 55 10.56578/pmdf010201 https://www.acadlore.com/article/PMDF/2024_1_2/pmdf010201 https://www.acadlore.com/article/PMDF/2024_1_1/pmdf010105 The study investigates the efficacy of eco-friendly cooling fluids, specifically vegetable oil and water mixtures, in the machining of EN19 steel, with a focus on enhancing performance metrics while promoting environmental sustainability. Machining parameters, including cutting speed, feed rate, and depth of cut (DOC), were analyzed for their effects on surface roughness, tool temperature, cutting forces, and material removal rate (MRR). The study employed a hybrid optimization approach, integrating Taguchi's orthogonal array (OA) method with grey relational analysis (GRA), to evaluate the effectiveness of these eco-friendly cutting fluids. The analysis revealed that spindle speed significantly influenced the MRR, while the DOC notably affected cutting force and tool temperature. The choice of coolant was found to have a considerable impact on surface roughness. Although the Taguchi method effectively optimized individual machining parameters, GRA provided a more comprehensive evaluation by synthesizing multiple performance metrics into a single index, achieving an accuracy of 80.17%, which surpassed the 72.44% accuracy of the Taguchi method. These findings underscore the potential of GRA to optimize the machining process of EN19 steel, offering substantial improvements in manufacturing efficiency and sustainability. The study highlights the importance of adopting eco-friendly practices in industrial machining, demonstrating that the integration of GRA and Taguchi methods can lead to more sustainable and efficient manufacturing processes. 03-30-2024 <![CDATA[ <p>The study investigates the efficacy of eco-friendly cooling fluids, specifically vegetable oil and water mixtures, in the machining of EN19 steel, with a focus on enhancing performance metrics while promoting environmental sustainability. Machining parameters, including cutting speed, feed rate, and depth of cut (DOC), were analyzed for their effects on surface roughness, tool temperature, cutting forces, and material removal rate (MRR). The study employed a hybrid optimization approach, integrating Taguchi's orthogonal array (OA) method with grey relational analysis (GRA), to evaluate the effectiveness of these eco-friendly cutting fluids. The analysis revealed that spindle speed significantly influenced the MRR, while the DOC notably affected cutting force and tool temperature. The choice of coolant was found to have a considerable impact on surface roughness. Although the Taguchi method effectively optimized individual machining parameters, GRA provided a more comprehensive evaluation by synthesizing multiple performance metrics into a single index, achieving an accuracy of 80.17%, which surpassed the 72.44% accuracy of the Taguchi method. These findings underscore the potential of GRA to optimize the machining process of EN19 steel, offering substantial improvements in manufacturing efficiency and sustainability. The study highlights the importance of adopting eco-friendly practices in industrial machining, demonstrating that the integration of GRA and Taguchi methods can lead to more sustainable and efficient manufacturing processes.</p> ]]> Sustainable Machining of EN19 Steel: Efficacy of Eco-Friendly Cooling Fluids and Hybrid Optimization Techniques rai sujit nath sahai pankaj k. jadhav sachin solanke shravan h. gawande doi: 10.56578/pmdf010105 Precision Mechanics & Digital Fabrication 03-30-2024 Precision Mechanics & Digital Fabrication 03-30-2024 2024 1 1 Article 41 10.56578/pmdf010105 https://www.acadlore.com/article/PMDF/2024_1_1/pmdf010105 https://www.acadlore.com/article/PMDF/2024_1_1/pmdf010104 The digitization of production processes in the manufacturing sector represents a pivotal transformation that fundamentally reshapes how companies achieve productivity, make informed decisions, and secure a competitive advantage. This research investigates the integration of Industry 4.0 technologies—including the Internet of Things (IoT), big data analytics, 3D printing, robotics, and artificial intelligence (AI)—within traditional manufacturing systems. The study focuses on three key dimensions driving digital transformation in manufacturing firms and examines their impact on digital platforms, which are increasingly critical for maintaining competitiveness in the digital age. The adoption of these platforms facilitates the seamless integration of Industry 4.0 technologies, thereby enhancing the growth potential and innovative capacity of manufacturing companies. This investigation involves a comprehensive analysis of data collected from 635 valid surveys across six countries—Serbia, Hungary, Poland, Slovakia, the Czech Republic, and Bulgaria—using Structural Equation Modeling (SEM). The findings confirm the significant influence of positive employee attitudes toward digitization and the intention to utilize digital tools on the successful adoption of Industry 4.0 technologies. These results underscore the necessity of fostering a culture that supports digital transformation, which, in turn, improves the efficiency and competitiveness of manufacturing firms. This study provides valuable insights into the future trajectory of digitization in the manufacturing sector, highlighting the essential role of digital platforms in the ongoing evolution of the industry. 03-30-2024 <![CDATA[ The digitization of production processes in the manufacturing sector represents a pivotal transformation that fundamentally reshapes how companies achieve productivity, make informed decisions, and secure a competitive advantage. This research investigates the integration of Industry 4.0 technologies—including the Internet of Things (IoT), big data analytics, 3D printing, robotics, and artificial intelligence (AI)—within traditional manufacturing systems. The study focuses on three key dimensions driving digital transformation in manufacturing firms and examines their impact on digital platforms, which are increasingly critical for maintaining competitiveness in the digital age. The adoption of these platforms facilitates the seamless integration of Industry 4.0 technologies, thereby enhancing the growth potential and innovative capacity of manufacturing companies. This investigation involves a comprehensive analysis of data collected from 635 valid surveys across six countries—Serbia, Hungary, Poland, Slovakia, the Czech Republic, and Bulgaria—using Structural Equation Modeling (SEM). The findings confirm the significant influence of positive employee attitudes toward digitization and the intention to utilize digital tools on the successful adoption of Industry 4.0 technologies. These results underscore the necessity of fostering a culture that supports digital transformation, which, in turn, improves the efficiency and competitiveness of manufacturing firms. This study provides valuable insights into the future trajectory of digitization in the manufacturing sector, highlighting the essential role of digital platforms in the ongoing evolution of the industry. ]]> Digital Transformation in Manufacturing: Enhancing Competitiveness Through Industry 4.0 Technologies isidora m. milošević olesea plotnic andrea tick zorana stanković adriana buzdugan doi: 10.56578/pmdf010104 Precision Mechanics & Digital Fabrication 03-30-2024 Precision Mechanics & Digital Fabrication 03-30-2024 2024 1 1 Article 31 10.56578/pmdf010104 https://www.acadlore.com/article/PMDF/2024_1_1/pmdf010104 https://www.acadlore.com/article/PMDF/2024_1_1/pmdf010103 This investigation addresses the issue of premature failure or damage to bearing components in aeroengines, which often results from the release of dissolved gases in the lubricant due to environmental pressure changes during operation. Employing the three-dimensional Reynolds equation and focusing on an ideal lubricating oil, a lubrication model for the engine camshaft's oil film was developed. The formation and extent of gaseous voids within plain bearings were analyzed. The study systematically explored how fit clearance and lubricating oil viscosity influence oil film pressure and thickness. It was found that a reduced fit gap increases the oil film pressure gradient while decreasing the film's thickness. Additionally, although variations in lubricating oil viscosity do not affect the distribution of oil film thickness, they significantly impact the pressure exerted on the oil film, with higher viscosities leading to increased pressures. These findings provide essential theoretical guidance for the safety assessment of aeroengine plain bearings. 03-30-2024 <![CDATA[ <p>This investigation addresses the issue of premature failure or damage to bearing components in aeroengines, which often results from the release of dissolved gases in the lubricant due to environmental pressure changes during operation. Employing the three-dimensional Reynolds equation and focusing on an ideal lubricating oil, a lubrication model for the engine camshaft's oil film was developed. The formation and extent of gaseous voids within plain bearings were analyzed. The study systematically explored how fit clearance and lubricating oil viscosity influence oil film pressure and thickness. It was found that a reduced fit gap increases the oil film pressure gradient while decreasing the film's thickness. Additionally, although variations in lubricating oil viscosity do not affect the distribution of oil film thickness, they significantly impact the pressure exerted on the oil film, with higher viscosities leading to increased pressures. These findings provide essential theoretical guidance for the safety assessment of aeroengine plain bearings.</p> ]]> Impact of Oil Film Dynamics on the Performance of Aeroengine Plain Bearings senlin liu yu wang doi: 10.56578/pmdf010103 Precision Mechanics & Digital Fabrication 03-30-2024 Precision Mechanics & Digital Fabrication 03-30-2024 2024 1 1 Article 24 10.56578/pmdf010103 https://www.acadlore.com/article/PMDF/2024_1_1/pmdf010103 https://www.acadlore.com/article/PMDF/2024_1_1/pmdf010102 Limitations inherent in conventional rule generation methodologies, particularly concerning knowledge redundancy and efficiency in product design, are addressed through the adoption of a rough set-based approach in this study. An enhancement to the Ant Colony Optimization (ACO) algorithm's information gain ratio is introduced by integrating a redundancy detection mechanism, which notably accelerates the convergence process. Furthermore, the application of a classification consistency algorithm effectively minimizes the number of attributes, facilitating the extraction of potential associative rules. Comparative validation performed on a public dataset demonstrates that the proposed attribute reduction approach surpasses existing methods in terms of attribute count reduction, reduction rate, and execution time. When applied to an automotive design case study, the approach yields rules with 100% coverage and accuracy, characterized by a reduced average number of attributes per rule. These findings underscore the superiority of the rough set-based methodology in generating product design rules, providing a robust framework that enhances both the precision and applicability of the design process. 03-30-2024 <![CDATA[ <p>Limitations inherent in conventional rule generation methodologies, particularly concerning knowledge redundancy and efficiency in product design, are addressed through the adoption of a rough set-based approach in this study. An enhancement to the Ant Colony Optimization (ACO) algorithm's information gain ratio is introduced by integrating a redundancy detection mechanism, which notably accelerates the convergence process. Furthermore, the application of a classification consistency algorithm effectively minimizes the number of attributes, facilitating the extraction of potential associative rules. Comparative validation performed on a public dataset demonstrates that the proposed attribute reduction approach surpasses existing methods in terms of attribute count reduction, reduction rate, and execution time. When applied to an automotive design case study, the approach yields rules with 100% coverage and accuracy, characterized by a reduced average number of attributes per rule. These findings underscore the superiority of the rough set-based methodology in generating product design rules, providing a robust framework that enhances both the precision and applicability of the design process.</p> ]]> Enhanced Rule Generation in Product Design Through Rough Set Theory and Ant Colony Optimization xianwei wang tichun wang doi: 10.56578/pmdf010102 Precision Mechanics & Digital Fabrication 03-30-2024 Precision Mechanics & Digital Fabrication 03-30-2024 2024 1 1 Article 11 10.56578/pmdf010102 https://www.acadlore.com/article/PMDF/2024_1_1/pmdf010102 https://www.acadlore.com/article/PMDF/2024_1_1/pmdf010101 In the automated production line for suspended insulators, precise alignment of the U-shaped notch in iron caps is crucial for effective gluing. This study introduces a system based on machine vision that automates the alignment process. The system initially preprocesses the images of iron caps to segment the U-shaped contour. It utilizes the method of quadratic maximum contour connectivity domain to accurately identify the target U-shaped region. The alignment process involves calculating the coordinates of the largest external rectangle's longest edge and the external circle's center point. These coordinates are instrumental in determining the necessary rotation angle for proper notch alignment. The fixture then adjusts the iron cap based on this calculated angle, ensuring precise alignment. Experimental validations of this system have demonstrated a notch alignment error within 0.5 degrees with 96.51% accuracy and an error within 1 degree with 100% accuracy. The algorithm's execution time is a swift 0.034 seconds. Both the error margins and operational speed satisfy the stringent requirements of the automatic production line. 03-30-2024 <![CDATA[ <p>In the automated production line for suspended insulators, precise alignment of the U-shaped notch in iron caps is crucial for effective gluing. This study introduces a system based on machine vision that automates the alignment process. The system initially preprocesses the images of iron caps to segment the U-shaped contour. It utilizes the method of quadratic maximum contour connectivity domain to accurately identify the target U-shaped region. The alignment process involves calculating the coordinates of the largest external rectangle's longest edge and the external circle's center point. These coordinates are instrumental in determining the necessary rotation angle for proper notch alignment. The fixture then adjusts the iron cap based on this calculated angle, ensuring precise alignment. Experimental validations of this system have demonstrated a notch alignment error within 0.5 degrees with 96.51% accuracy and an error within 1 degree with 100% accuracy. The algorithm's execution time is a swift 0.034 seconds. Both the error margins and operational speed satisfy the stringent requirements of the automatic production line.</p> ]]> Automated Alignment of U-Notch in Iron Caps Using Machine Vision: A System for Suspended Insulators kui kang huiyu zhang yu wang doi: 10.56578/pmdf010101 Precision Mechanics & Digital Fabrication 03-30-2024 Precision Mechanics & Digital Fabrication 03-30-2024 2024 1 1 Article 1 10.56578/pmdf010101 https://www.acadlore.com/article/PMDF/2024_1_1/pmdf010101