This study aims to illustrate the design, fabrication, and optimisation of an ultrasonic welding (UW) machine to join copper wires with non-woven PVC textiles as smart textiles. The study explicitly evaluates UW parameters’ impact on heat generation, joint strength, and electrical properties, with a comprehensive understanding of the process dynamics and developing a predictive model applicable to smart textiles. The methodological approach involved designing and manufacturing an ultrasonic piezoelectric transducer using ABAQUS finite element analyses (FEA) software and constructing a UW machine for the current purpose. The full factorial design (FFD) approach was employed in experiments to systematically assess the influence of welding time, welding pressure, and copper wire diameter on the produced joints. Experimental data were meticulously collected, and a backpropagation neural network (BPNN) model was constructed based on the analysis of these results. The results of the experimental investigation provided valuable insights into the UW process, elucidating the intricate relationship between welding parameters and heat generation, joint strength, and post-welding electrical properties of the copper wires. This dataset served as the basis for developing a neural network model, showcasing a high level of accuracy in predicting welding outcomes compared to the FFD model. The neural network model provides a valuable tool for controlling and optimising the UW process in the realm of smart textile production.

In the real world, the problems mostly are complex; more precisely, the problems generally are nonlinear or large scale other than if it was mandatory to resolve it under certain constraints, and that is common in engineering design problems. Therefore, the complexity of problem plays a critical role in determining the computational time and cost. Accordingly, a novel algorithm called inner-outer array is proposed in this paper. It depends on the design of parameters and then tolerance design as one of design of experiment stages. In this work, the inner-outer algorithm is used to solve real-world optimization problems to choose the preferable feasible regions of the entire search domain. Numerical results are documented and compared based on four well-known constrained mechanical engineering issues. It can be concluded that the performance of inner-outer algorithm is good to optimize constrained engineering problems, but it still needs some enhancements in the future work.

One issue with optimization is that when the problem becomes more complicated, the stand-alone optimizer is unable to obtain the global optimal value consistently. That is why the Inner-Outer Array is developed to help the optimizer find a global optimum without going too deeply into the optimizer's parameter settings, which are not always applicable. As a result, this paper presents a novel hybridization approach combining Inner-Outer Array (IOA) and Genetic Algorithm (GA). IOA is a critical step in the IOA-GA method since it aids in the discovery of the global or near-global optimal solution. The developed approach, known as the Inner-Outer Array (IOA), is based on two stages of experimental design: parameter design and tolerance design. Depending on the number of variables and constraints vs. problem size, this approach has one inner array and one or more outer arrays. During the preceding few decades, GAs have proven to be an effective technique for solving real-world optimization problems. In the case of a wide solution space and multiple local optima, however, GAs cannot guarantee a global optimum solution. Here comes the role of the exploratory ability of IOA in scoping the search space, including guiding GA to reach the global or near global optimal result, which is the purpose of this work. More than 15 complex engineering optimization applications, inspired by real problems in the field of mechanical engineering, are used to verify the performance of the proposed method IOA-GA. This research paper used two issues from the literature.

This work investigates how ultrasonic vibration can enhance friction stir welding (FSW) of an AA6082-T6 aluminium alloy and develops a data-driven tool to predict joint performance from process settings. A custom ultrasonic transducer and horn were designed and tuned using finite element modal and harmonic analyses, confirming a strong longitudinal resonance near 27.9 kHz with a tip amplitude of about 46 µm. A 27-run factorial experiment varied tool rotation (600–900 rpm), welding speed (45–55 mm/min), and plunge depth (0.10–0.25 mm). Welded joints were assessed using tensile strength and Vickers hardness. Four predictive models, support vector regression (SVR), Gaussian process regression (GPR), artificial neural networks (ANNs), and multiple linear regression (MLR) were trained and compared under five-fold cross-validation. The best joint quality was obtained at 900 rpm, 55 mm/min, and a 0.25 mm plunge depth, yielding a tensile strength of 188.7 MPa and a hardness of 102 HV. Overall, MLR provided the strongest predictive performance while remaining interpretable (UTS R² = 0.81, RMSE = 11.84 MPa; hardness R² = 0.67, RMSE = 2.36 HV), matching the ANN for UTS prediction and outperforming the ANN, GPR, and SVR for hardness. A coupling physics-based ultrasonic design with an interpretable predictive model offers a practical route to reduce trial and error, improve parameter selection, and accelerate the process development for ultrasonic vibration-assisted FSW of aluminium alloys; however, modest models can outperform complex ones when the dataset is limited.

The advancement of high-performance thermoelectric (TE) materials is sometimes hindered by the inadequate capacity of conventional experimental methods to clarify complicated processing–property interactions. This study proposed a data-driven computational–experimental framework that integrates Response Surface Methodology (RSM), based on a Central Composite Design (CCD), with a Multi-Objective Genetic Algorithm (MOGA). This hybrid technique proposed to optimize the thermoelectric performance of Bi₈₅Sb₁₅ alloys processed via Equal Channel Angular Extrusion (ECAE). Three critical parameters are measurement temperature, processing temperature, and number of ECAE passes were systematically analysed to quantify their influence on electrical resistivity (ρ), thermal conductivity (k), Seebeck coefficient (S), power factor (PF), and figure-of-merit (ZT). RSM was used to generate reliable surrogate models, and MOGA was utilized to quickly find the best trade-offs between the different TE responses. The optimal solution shows that eight ECAE passes at a processing temperature of 73 °C give a maximum ZT of 0.32, which is 12% better than what was measured in the experiment. These results show the effectiveness of the proposed data-driven process optimization approach (RSM–MOGA) for accelerating the design and optimization of Bi–Sb thermoelectric material. It also provides a basic structure for making new TE materials with superior performance. The suggested data-driven optimization methodology supports global goals in clean energy, energy efficiency, and engineering for sustainable materials. This work helps improve waste-heat recovery, solid-state cooling, and decentralized energy solutions by making it easier to rapidly design TE materials with effective ZT.