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The effects of processing conditions and holding time on the direct bonding (DBC) of lotus-type porous copper to alumina substrates were systematically investigated. The evolution of copper morphology and the resulting shear strength were evaluated under varying pressures (0.3–0.6 Torr) and bonding durations (5–160 min) at a fixed bonding temperature. It was found that pressure within the tested range exerted a negligible influence on joint quality, as direct bonding occurred consistently. In contrast, holding time was found to be a critical factor: a duration of 10 min yielded optimal bonding with high shear strength while preserving the porous structure, whereas shorter times led to incomplete bonding, and longer times caused structural collapse due to liquid-phase flow. The oxidation behavior, governed by parabolic growth kinetics, was identified as the primary mechanism controlling morphological evolution. These findings provide practical guidance for optimizing DBC bonding of porous copper in power semiconductor applications, balancing joint strength and structural integrity.

The use of adhesive joints in various industrial applications has become increasingly popular due to their beneficial characteristics, including their high strength-to-weight ratio, design flexibility, limited stress concentrations, planar force transfer, good damage tolerance, and fatigue resistance. However, finding the best process parameters for adhesive bonding can be challenging. This optimization problem is inherently multi-objective, aiming to maximize break strength while minimizing cost and constrained to avoid any visual damage to the materials and ensure that stress tests do not result in adhesion-related failures. Additionally, testing the same process parameters several times may yield different break strengths, making the optimization process uncertain. Conducting physical experiments in a laboratory setting is costly, and traditional evolutionary approaches like genetic algorithms are not suitable due to the large number of experiments required for evaluation. Bayesian optimization is suitable in this context, but few methods simultaneously consider the optimization of multiple noisy objectives and constraints. This study successfully applies advanced learning techniques to emulate the objective and constraint functions based on limited experimental data. These are incorporated into a Bayesian optimization framework, which efficiently detects Pareto-optimal process configurations under strict budget constraints.

Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and performance must be achieved with the help of new technologies. This work focused on improving metal–metal bonding performance by applying SLJ experiments, finite element models (FEMs), and the Xgboost machine learning (ML) algorithm. The importance ranking of process parameters on tensile–shear strength (TSS) was evaluated with the interpretation toolkit SHAP (Shapley additive explanations) and it optimized reasonable bonding process parameters. The validity of the FEM was verified using SLJ experiments. The Xgboost models with 70 runs can achieve better prediction results. According to the degree of influence, the process parameters affecting the TSS ranked from high to low are roughness, adhesive layer thickness, and lap length, and the corresponding optimized values were 0.89 μm, 0.1 mm, and 27 mm, respectively. The experimentally measured TSS values increased by 14% from the optimized process parameters via the Xgboost model. ML methods provide a more accurate and intuitive understanding of process parameters on TSS.

The objectives of this study were to analyze the bonding criteria for friction stir spot welding (FSSW) using a finite element analysis (FEA) and to determine the optimal process parameters using artificial neural networks. Pressure-time and pressure-time-flow criteria are the bonding criteria used to confirm the degree of bonding in solid-state bonding processes such as porthole die extrusion and roll bonding. The FEA of the FSSW process was performed with ABAQUS-3D Explicit, with the results applied to the bonding criteria. Additionally, the coupled Eulerian–Lagrangian method used for large deformations was applied to deal with severe mesh distortions. Of the two criteria, the pressure-time-flow criterion was found to be more suitable for the FSSW process. Using artificial neural networks with the bonding criteria results, process parameters were optimized for weld zone hardness and bonding strength. Among the three process parameters used, tool rotational speed was found to have the largest effect on bonding strength and hardness. Experimental results were obtained using the process parameters, and these results were compared to the predicted results and verified. The experimental value for bonding strength was 4.0 kN and the predicted value of 4.147 kN, resulting in an error of 3.675%. For hardness, the experimental value was 62 Hv, the predicted value was 60.018 Hv, and the error was 3.197%.

The implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus maintaining Nugget region integrity. This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). Studies on Diffusion Bonding reveal that ANN and Generic Algorithms can predict outcomes with an accuracy range of 85 – 99%, while Response Surface Methodology such as Optimization Strategy can achieve up to 95 percent confidence levels in improving bonding strength and optimizing process parameters. Using ANNs for FSW gives an average percentage error of about 95%, but using metaheuristics refined it at an incrementally improved accuracy rate of about 2%. In UW, ANN, Hybrid ANN, and ML models predict output parameters with accuracy levels ranging from 85 to 96%. Integrating AI techniques with optimization algorithms, for instance, GA and Particle Swarm Optimization (PSO) significantly improves accuracy, enhancing parameter prediction and optimizing UW processes. ANN’s high accuracy of nearly 95% compared to other techniques like FL and ML in predicting welding parameters. HM exhibits superior precision, showcasing their potential to enhance weld quality, minimize trial welds, and reduce costs and time. Various emerging hybrid methods offer better prediction accuracy.

A hybrid composite biopolymer of magnetic chitosan-fly ash/Fe3O4 (CS-FA/Fe3O4) was prepared to be an effective composite biosorbent for the removal of reactive orange 16 (RO16) dye from aqueous media. Various analytical techniques such as XRF, BET, XRD, FTIR, and SEM–EDX were utilized to characterize of CS-FA/Fe3O4 composite. The effects of adsorption process parameters namely adsorbent dose (A: 0.04–0.12 g), solution pH (B: 4–10), temperature (C: 30–50 °C), and time (E: 20–90 min) were optimized by using Box–Behnken design (BBD) in response surface methodology (RSM). The experimental results indicate that the highest RO16 removal was 73.1% by significant interaction between BC (p-value = 0.0002) and AD (p-value = 0.022). The optimum RO16 dye removal conditions were recorded at solution pH ~ 4, adsorbent dose (0.08 g), temperature (30 °C), and time (55 min). The adsorption process was well described by pseudo-second order (PSO) kinetic and Freundlich isotherm model. The adsorption capacity of CS-FA/Fe3O4 composite for RO16 dye was 66.9 mg/g at 30 °C. The mechanism of the RO16 dye adsorption included many interactions such as electrostatic, n–π interaction, H-bonding, and Yoshida H-bonding. Furthermore, the CS-FA/Fe3O4 composite exhibited a high ability to separate from the aqueous solution after adsorption process by external magnetic field.

The advent of digital concrete fabrication calls for advancing our understanding of the interaction of 3D printing with material rheology and print parameters, in addition to developing new measurement and control techniques. Thixotropy is the main challenge associated with printable material, which offers high yield strength and low viscosity. The higher the thixotropy, the better the shape stability and the higher buildability. However, exceeding a minimum value of thixotropy can cause high extrusion pressure and poor interface bond strength if the printing parameters are not optimized to the part design. This paper aims to investigate the effects of both material and process parameters on the buildability and inter-layer adhesion properties of 3D printed cementitious materials, produced with different thixotropy and print head standoff distances. Nano particles are used to increase the thixotropy and, in this context, a lower standoff distance is found to be useful for improving the bond strength. The low viscosity “control” sample is unaffected by the variation in standoff distances, which is attributed to its flowability and low yield stress characteristics that lead to strong interfacial bonding. This is supported by our microscopic observations.

The gold wire lead bonding process is a key technology to realize the electrical connection between the chip and the external control circuit to be completed. This paper introduces the basic concept of lead bonding technology and analyzes the process parameters affecting the quality of lead bonding, mainly including ultrasonic power, ultrasonic time, and bonding pressure. The orthogonal test method is used to investigate the process parameters affecting the bonding quality of 25 μm gold wire. The optimal combination of process parameters A 3 B 2 C 2 is determined, i.e., the ultrasonic power value is 90 mW, the ultrasonic time is 20 ms, and the bonding pressure is 40 gf, which improves the reliability of the wire bonding process.

Bamboo filament decorated board is a new kind of bamboo and wood composite material. In this material, melamine-urea-formaldehyde (MUF) modified resin impregnated paper is used as the bonding material between bamboo filament decoration material and finger joint plate. This research investigated the effects of hot pressing temperature, time, and pressure on surface bonding properties of bamboo filament decorated board, and the optimum process parameters were determined. With the surface bonding strength as the evaluation index, the response surface analysis was used to optimize the design of the optimal hot pressing process. The optimum surface bonding strength of 1.13 MPa was achieved with the process parameters of 130 s (hot pressing time), 148 °C (hot pressing temperature), and 2.00 MPa (hot pressing pressure). The experimental values were in good agreement with the predicted ones, and the relative error was less than 5%, showing the optimized result.

In conventional thermal spraying, the spray particles are partially or fully molten when they deposit on the substrate. Cold spraying, in contrast, uses less thermal and more kinetic energy. In this process, solid particles impact on the substrate at high velocities and form excellent coatings. Due to comparatively low temperatures and typically inert process gases, cold spraying is particularly suitable for heat and oxidation sensitive materials. In recent years, modeling and computational methods have been widely used to study this relatively new spraying process, particularly to describe impact conditions of particles, to improve nozzle design, and to provide a better understanding of the thermo-mechanical processes that lead to particle bonding and deposition. This paper summarizes the state of the art in these theoretical studies, alongside a comprehensive description of the process. The paper also discusses the prediction of coating properties in the light of modeling combined with experimental investigations.