MTNN based meta-modeling and PSO driven multi-response optimization of FSW parameters for enhancing mechanical attributes of AZ80A Mg alloy joints
Year: 2026
Type: article
Abstract: ABSTRACT In this work, an interpretable machine learning framework was developed for optimizing friction stir welding (FSW) parameters during joining of AZ80A Mg alloy plates for lightweight structural applications. The proposed strategy integrates a multi-task neural network (MTNN) with particle swarm optimization (PSO) to enhance tensile strength (TS) and hardness (H). Welding speed (WS), rotational speed (RS), axial force (AF), tool tilt angle (TA), and pin geometry (PG) were considered as input variables. A face-centered central composite design was adopted for generating training data. MTNN architecture effectively captured nonlinear interactions among process variables and mechanical responses through shared feature learning and task-specific prediction branches, achieving prediction accuracies (R²) of 95.13% and 93.35% during training, and 86.57% and 84.02% during testing for TS and H, respectively, with the lowest mean prediction deviations among all evaluated models. SHapley Additive exPlanations (SHAP) analysis revealed WS, AF, and tapered cylindrical (TC) pin geometry significantly influenced joint performance. Subsequently, PSO was coupled with the trained MTNN to identify optimal parameter combinations yielding superior TS and H, and experimentally validated. SEM fractography confirmed ductile fracture behavior and Compared with RR, SVR, ANN, and RFR models, MTNN–PSO framework demonstrated lower prediction error, improved robustness, and enhanced interpretability.
Source: Matéria (Rio de Janeiro)
Institution: Saveetha University
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7204534601