A Predictive Model for Weld Properties in AA-7075-FSW: A Heterogeneous AMIS-Ensemble Machine Learning Approach

Abstract

This study addresses the research gap in materials science by developing an integrated predictive model for Ultimate Tensile Strength (UTS), Maximum Hardness (MH), and Heat Input (HI) in AA-7075 Friction Stir Welding (FSW). The aim is to enhance welding procedures, particularly in high-precision industries like aerospace and automotive. By incorporating four control parameters (Tilt Angle, Rotation Speed, Welding Speed, and Shoulder Diameter) and utilizing a Long Short-Term Memory (LSTM) machine learning model, a Heterogeneous Ensemble Machine Learning (He-EM) approach is developed. The Artificial Multiple Intelligence System (AMIS) optimizes the decision fusion strategy of each technique, ensuring the model's effectiveness. Validated using diverse experimental design methods and three datasets, the proposed AMIS He-EM model outperforms existing techniques (GPR, SVM, HE-UWE, and HE-WEDE) by significant margins (35.12%, 24.92%, 22.31%, and 15.48% respectively). The model's robustness is demonstrated, as its performance remains consistent across different experimental design methods. The key finding of this research is the remarkable improvement in accuracy for predicting UTS, MH, and HI in AA7075 FSW. This study highlights the importance of incorporating four control parameters and utilizing the D-optimal design for efficient exploration of the input parameter space. The implications of this research are profound, offering opportunities to optimize welding procedures, improve product performance, and streamline manufacturing processes in industries relying on AA7075 FSW.