Machine learning classification applied to the effect of AFSD process parameters on tensile properties

Abstract

Additive Friction Stir Deposition (AFSD) is a key technology in additive manufacturing, where process parameters greatly impact deposition layer properties. Currently, there is no established method for systematically improving these properties through parameter investigation. This study addresses this by applying six machine learning (ML) classification algorithms to a dataset of 130 samples, classifying the ultimate tensile strength (UTS) based on feed rate, rotational speed, and downforce. The Support Vector Machine (SVM) algorithm achieved the highest accuracy at 95.3%. This research provides a precise method for classifying AFSD process parameters and demonstrates the potential of ML techniques for optimizing manufacturing processes, presenting a novel approach to enhancing AFSD deposition layer performance.