An Approach for Defect Detection in Friction Stir Welding Based on the Welding Force and Machine Learning

Abstract

This study leverages multiple machine learning algorithms for defect detection in friction stir welding (FSW), utilizing force-derived features as model inputs. Furthermore, the underlying relationships between welding forces and defect formation were systematically investigated, alongside an evaluation of the efficacy of force-feature-driven defect detection models. Results indicated that the variations in the averages and waveforms in the traverse force (Fx), lateral force (Fy) and plunge force (Fz) are highly responsible for the defect formation in FSW joints, such that an increase in Fy causes waveform distortions in Fx and Fy. Fyavg is the most important feature for the defect formation for 724 sets of experimental data. Defect detection based on thresholding of Fyavg and Fzavg achieves an accuracy of 80.3%. In contrast, four machine learning algorithms—Decision Tree (DT), K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—were employed to construct defect detection models using the extracted force features as inputs, yielding accuracies of 93.5%, 97.5%, 95.7% and 94.9%, respectively. These findings further elucidate the underlying mechanics, that is, Fx and Fy primarily originated from the extrusion and shear forces induced by the probe’s rotation and traverse, whereas Fz was predominantly attributed to the compressive action of the shoulder.