A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition

Abstract

Additive friction stir deposition (AFSD) provides strong flexibility and better performance in component design, which is controlled by the process parameters. It is an essential and difficult task to tune those parameters. The recent exploration of machine learning (ML) exhibits great potential to obtain a suitable balance between productivity and set parameters. In this study, ML techniques, including support vector machine (SVM), random forest (RF) and artificial neural network (ANN), are applied to predict the mechanical properties of the AFSD-based AA6061 deposition. Expect for the stable parameters (temperature, force and torque) in situ monitored by the self-developed process-aware kit during the AFSD process and the other factors (rotation speed, traverse speed, feed rate and layer thickness) are also set as input variables. The output variables are microhardness and ultimate tensile strength (UTS). Prediction results show that the ANN model performs the best prediction accuracy with the highest R² (0.9998) and the lowest mean absolute error (MAE, 0.0050) and root mean square error (RMSE, 0.0063). Furthermore, analysis suggests that the feed rate (24.8%/24.1%) and layer thickness (25.6%/26.6%) indicate a higher contribution that affects the mechanical properties.

Keywords

additive friction stir deposition / in-situ monitored / machine learning / mechanical properties / prediction

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2024 2024 The Authors. Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.