Machine learning classification-based approach for mechanical properties of friction stir welding of copper

The paper presents a machine learning classification-based model for a friction stir welded copper's mechanical properties. The models train and test 119 experimental data for the pure copper system to study the effect of process parameters on FSW copper joints' mechanical properties. Four classification models were employed to analyze the impact of process parameters on the mechanical properties. The deep learning-based neural network model exhibited the highest accuracy of 94%. The machine learning model suggested that tool features and design were the most important parameters influencing the joint's mechanical property. (C) 2021 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.

Automated summary

This study employed machine learning models to analyze the impact of process parameters on the mechanical properties of friction stir welded copper joints, with the neural network model achieving the highest accuracy of 94%. The machine learning model suggested that tool features and design were the most important parameters influencing the joint's mechanical properties.