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.

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 (S_J), 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.

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.

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.

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.

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.

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.