This investigation delves into the noise attenuation capabilities of an innovatively designed muffler, which integrates additional piping and perforation to augment sound reflection. The enhanced muffler's design was rigorously simulated using the Helmholtz equation through the application of COMSOL Multiphysics software, aiming to delineate its acoustic performance relative to conventional models. The analysis underscored the superior efficacy of the optimized model in elevating transmission loss, diminishing acoustic pressure, and concurrently attenuating noise and frequency levels. A comparative evaluation of the transmission loss between the traditional and the novel muffler revealed a significant amelioration in the latter, highlighting its advanced noise reduction capabilities. The study further illuminated that exhaust pressure and back pressure contribute to acoustic wave generation, prompting the optimization of the muffler design to mitigate pressure, thereby circumventing potential damage. Notably, despite the analytical complexity, the construction of the proposed muffler remains straightforward, representing a pivotal advantage. This research contributes to the acoustic engineering field by presenting a muffler design that not only significantly reduces noise pollution but also demonstrates an ease of construction, making it a viable solution for widespread application. The findings advocate for the muffler's potential in enhancing acoustic comfort and environmental compliance in automotive and industrial settings.
The Spherical Fuzzy Set (SFS) framework extends the Picture Fuzzy Set (PFS) concept, offering enhanced precision in handling data characterized by conflict and uncertainty. Furthermore, similarity measures (SMs) are crucial for determining the extent of resemblance between pairs of fuzzy values. While existing SMs evaluate similarity by measuring the distance between values, they sometimes yield results that are illogical or unreasonable, due to certain properties and operational complexities. To address these anomalies, this paper introduces a parametric similarity measure based on three adjustable parameters ($\alpha_1, \alpha_2, \alpha_3$), allowing decision-makers to fine-tune the measure to suit various decision-making styles. This paper also scrutinizes existing SMs from a mathematical standpoint and demonstrates the efficacy of the proposed SM through mathematical modeling. Finally, we apply the proposed SM to tackle Multi-Attribute Decision-Making (MADM) problems. Comparative analysis reveals that our proposed SM outperforms certain existing SMs in the context of SFS-based applications.
This research employs a data-driven approach to optimize the plasma arc cutting process. The evaluation of cut quality is based on six output characteristics, while the input parameters include stand-off distance, cutting current, and cutting speed. The output metrics consist of the material removal rate (MRR), surface roughness, bevel angle, slag formation, kerf width, and heat-affected zone (HAZ). Given the complexity of the process and the multitude of involved processing parameters, it is imperative to develop an optimization model to ensure the production of undisturbed structures. The primary aim of this study is to identify the most critical factors that facilitate optimal conditions for plasma arc cutting. The research goal is to determine the influence of input parameters on the plasma arc cutting quality using an adaptive neural fuzzy inference system (ANFIS). It has been found that the material removal rate (MRR), surface roughness, bevel angle, slag formation, kerf width, and heat-affected zone (HAZ) are predominantly affected by the interplay of cutting current and stand-off distance. Ideally, the best predictive model for various attributes, such as MRR, bevel angle, slag formation, surface roughness, kerf width, and HAZ, is one that synergistically combines cutting current and stand-off distance. This study, which evaluates multiple input parameters simultaneously, is expected to attract significant attention as it represents a pioneering small-scale investigation in the field.
In the field of industrial buildings, notably within warehouse settings, the optimization of floor space emerges as a paramount concern. The deployment of equipment facilitating continuous transport is mandated to not only augment throughput but also to economize on spatial allocation. Within this spectrum, continuous vertical conveyors, particularly of the paternoster variety, have been adopted as a quintessential solution. This study delineates the design intricacies of a paternoster continuous vertical conveyor, elucidating the methodology employed in calculating its maximal throughput, movement resistance, and the requisite power for its electric motor. Through a rigorous analytical approach, the performance of the paternoster conveyor is meticulously evaluated and juxtaposed against alternative continuous vertical conveyor systems. The findings underscore the paternoster conveyor's efficacy in achieving high throughput efficiency while conserving space, thus reaffirming its utility in industrial warehousing. The evaluation employs comparative metrics to highlight the paternoster system's superiority in specific operational parameters. This analysis contributes to the corpus of knowledge by providing a comprehensive examination of paternoster conveyors, thereby aiding in the selection of efficient transport solutions within the constraints of warehouse space optimization.
Against the backdrop of current market demands for a variety of products in small batches, traditional single-variety assembly lines are transitioning to variable production lines to accommodate the manufacturing of multiple similar products. This paper discusses the production unit as a microcosm of the variable production line, which boasts advantages such as smaller line scale, short setup times for changeovers, and ease of product scheduling. A mathematical model for splitting variable production lines into production units is established, with solutions at two levels: resource allocation and product scheduling. The upper-level model focuses on determining the number of production units and the distribution scheme of operators and equipment across multiple channels; the lower-level model addresses the product allocation problem, which is characterized by multiple stages, divisibility, variable batch sizes, and minimum batch size constraints. The solution approaches include a branch and bound method for small-scale problems to obtain optimal solutions, and an improved particle swarm optimization (PSO) algorithm for medium to large-scale problems to find near-optimal solutions. The innovation of the paper lies in the construction of the variable production line splitting model and the optimization algorithms for resource allocation and product scheduling.
In the realm of smart vehicle navigation, both in known and unknown environments, the crucial aspects encompass the vehicle's localization using an array of technologies such as GPS, cameras, vision systems, laser, and ultrasonic sensors. This process is pivotal for effective motion planning within the vehicle's free configuration space, enabling it to adeptly avoid obstacles. The focal point of such navigation systems lies in devising a path from an initial to a target configuration, striving to minimize the path length and the time taken, while simultaneously circumventing obstacles. The application of metaheuristic algorithms has been pivotal in this regard. These algorithms, characterized by their ability to exploit initial solutions and explore the environment for feasible pathways, have been extensively utilized. A significant body of research in robotics and automation has focused on evaluating the efficacy of population-based algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization Algorithm (WOA). Additionally, trajectory-based methods such as Tabu Search (TS) and Simulated Annealing (SA) have been scrutinized for their proficiency in identifying short, feasible paths among the plethora of solutions. There has been a surge in the enhancement and modification of these algorithms, with a multitude of hybrid metaheuristic algorithms being proposed. This review meticulously examines various metaheuristic algorithms and their hybridizations, specifically in their application to the path planning challenges faced by smart vehicles. The exploration extends to the comparison of these algorithms, highlighting their distinct advantages and limitations. Furthermore, the review delves into potential future directions in this evolving field, emphasizing the continual refinement of these algorithms to cater to the increasingly complex demands of smart vehicle navigation.
The burgeoning importance of digitalization and cyber-physical manufacturing systems in the industrial sector is undeniable, yet discussions around viable solutions for small and medium-sized enterprises remain scant. These enterprises often face constraints in replacing extant machinery or implementing extensive IT upgrades, despite the availability of skilled engineering personnel. In response to this gap, an illustrative use case involving the application of Digital Twins (DT) to legacy systems is delineated, encompassing a detailed exploration of necessary hardware and software components, alongside pertinent considerations for implementation design. The establishment of a symbiotic relationship between the physical and digital realms is underscored as imperative, necessitating a granular understanding of the system to uncover opportunities and constraints for intervention. Such understanding is posited as a critical determinant of the DT's utility. This case study, situated within the Cyber-Physical Manufacturing Systems Laboratory at Széchenyi István University, serves to elucidate these principles and contribute to the discourse on smart manufacturing solutions for legacy systems.
In military operations, the proficient overcoming of water barriers is paramount, with sub-optimal execution potentially leading to significant human and equipment casualties. In this context, global armed forces accord considerable emphasis to the selection of appropriate mechanisms for water obstacle overcoming. This study elucidates the adoption of a Multi-Criteria Decision-Making (MCDM) approach for the selection of optimal pontoon bridge sets for military applications. Criteria identification was undertaken by seven distinguished experts, leading to the determination of weight coefficients using the Defining Interrelationships Between Ranked criteria II (DIBR II) method. Expert assessments were subsequently aggregated utilizing the Normalized Weighted Bonferroni Mean (NWBM) operator. The Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) method, operationalized within the Fermatean Fuzzy (FF) environment, was harnessed for the discernment of the best alternative. An analysis of the sensitivity of the study's findings with respect to variations in criteria weighting, coupled with a comparative exploration, led to the inference that the proposed MCDM model boasts stability. However, it was noted that the model exhibits sensitivity to shifts in criteria weight coefficients, underscoring its utility as a valuable aid for decision-makers, especially in the domain of pontoon bridge set selection.
Road transport emerges as a crucial segment of the transportation system, demanding comprehensive analyses of operational performance across passenger and freight domains. This investigation delineates a meticulous multi-criteria analysis of Serbian passenger and freight road transport, relying on data extracted from the Annual Statistical Reports promulgated by the Statistical Office of the Republic of Serbia during 2015-2021. Initially, a compendium of eight pertinent criteria, namely carrying capacity, total number of passenger and tonne-kilometres, employee count, generating power, fuel consumption, and foreign currency receipts, is identified, with a subsequent emphasis placed on six criteria necessitating multi-criteria analysis, applicable cohesively to both passenger and freight transport sectors. Weighting coefficients for each criterion are calculated employing the entropy method, while a multi-criteria ranking of the operational performance of road transport is devised through the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The quintessence of this research lies in the execution of a novel multi-criteria analysis with an aspiration to architect a hierarchy regarding the operational performance within the scrutinised timeframe of road transport in Serbia.
Innovative lightweight smart structures incorporating piezoelectric material-based active elements, both as sensors and actuators, have been identified to present manifold advantages over traditional passive systems. Such structures have become intrinsically integrated into smart mechatronic systems, necessitating advanced design, testing, and control techniques. Real-time simulation of shell-type deformable objects, especially when employing the finite element method for non-linear analysis and control, has been challenging due to the extensive computational demand. Presented herein is an efficacious implementation leveraging machine learning with the isogeometric finite element formulation. This implementation focuses on shell-like smart mechatronic structures crafted from composite laminates comprising piezoelectric layers, which are characterised by electro-mechanical coupling. The foundation for the shell kinematics is derived from the Mindlin-Reissner assumptions, effectively incorporating transverse shear effects. While the inclusion of machine learning facilitates real-time efficient operations, the isogeometric finite element analysis (FEA) introduces pronounced advantages over conventional finite element method (FEM), also serving as a valuable source of offline data crucial for the training phases of machine learning algorithms. A piezo-laminated semicircular arch has been analysed to exemplify the effectiveness and performance of the presented methodology. Explorations into further machine learning techniques and intelligent control schemes are also contemplated.
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