Optimization of Refill Friction Stir Spot Welded AA2024-T3 Using Machine Learning

• Main Authors: Amancio-Filho, S.T (Author), de Carvalho, W.S (Author), Effertz, P.S (Author), Guimarães, R.P.M (Author), Saria, G. (Author)
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2022
• Subjects: AA2024-T3; Aluminum alloys; Dissimilar materials; Friction; Friction stir; Friction stir spot welding; Friction stir welding; Lap shear; machine learning; Machine learning; Optimisations; optimization; polynomial regression; Polynomial regression; refill friction stir spot welding; Refill friction stir spot welding; Rotational speed; Shear flow; Shear force; Spot welded; Spot welding
• Online Access: View Fulltext in Publisher

 The Refill Friction Stir Spot Welding is an innovative spot like solid state process befitting of overlap joint configurations of similar and dissimilar materials. This process caught the interest and is rapidly growing in the aerospace sector due to its potential to substitute traditional mechanical fasteners, surpassing their mechanical performance while maintaining the so desired lightweight “rationale.” In the current study, process parameters, namely plunge depth, plunge time and rotational speed, are optimized in order to obtain the highest Ultimate Lap Shear Force (ULSF) of 2024-T3 Aluminum Alloy similar joints. The optimization campaign was carried out using a second order multivariate polynomial regression machine learning (ML) algorithm. The trained ML model was able to generalize and accurately predict the Ultimate Lap Shear Force on the holdout set, having a (Formula presented.) of 88.0%. Moreover, the model suggested an optimum parameter combination (Rotational Speed = 2,310 rpm, Welding Time = 5.3 s and Plunge Depth = 2.6 mm) from which the predicted maximum ULSF was computed. Confirmation tests were carried out to evaluate the agreement between the predicted and the experimental values. Copyright © 2022 Effertz, de Carvalho, Guimarães, Saria and Amancio-Filho.