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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.