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.

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.

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.

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.

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.

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.

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.

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.

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.

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.