Development of a fuzzy logic model for predicting the quality of micro friction stir spot welding (µFSSW) using particle swarm optimization

Abstract

Micro Friction Stir Spot Welding (µFSSW) is crucial in microelectronics and precision manufacturing. It requires a comprehensive understanding of the complex connections between various parameters to achieve the highest quality welds. This study aims to improve the prediction of µFSSW weld quality by incorporating advanced optimization techniques. Fuzzy Logic Optimization is used to model uncertainties, and Particle Swarm Optimization (PSO) is employed to fine-tune parameters for improved accuracy. The fuzzy logic system utilizes Gaussian functions as membership functions, organized with nine rule bases. The results clearly demonstrate that the fuzzy logic model greatly enhances accuracy when combined with Particle Swarm Optimization. The refined model improves precision for pin diameter, shoulder diameter, Thermo-Mechanically Affected Zone (TMAZ) area, and cross-tensile strength. The PSO-optimized model shows lower accuracy in predicting plunge depth and shear tensile strength. The ongoing decline in Root Mean Square Error (RMSE) values highlights the complexity of the results. The optimization significantly improves the model’s ability to predict specific weld quality metrics, as demonstrated by the pin diameter’s reduced RMSE value of 0.07. The collective results showcase an optimized Fuzzy Logic System (FLS) model adept at accurately predicting µFSSW weld quality, demonstrating adaptability across diverse conditions. The discernible increase in accuracy, reaching up to 76 % following the optimization of the fuzzy logic model with PSO, serves as a testament to the efficacy of the employed methodologies in advancing the precision and reliability of µFSSW weld quality predictions