Optimization of Refill Friction Stir Spot Welded AA2024-T3 Using Machine Learning - Effertz, P. S.; de Carvalho, W. S.; Guimarães, R. P. M.; Saria, G.; Amancio-Filho, S. T. (Frontiers in Materials, 2022)
The Refill Friction Stir Spot Welding (RFSSW) 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 2nd order multivariate polynomial regression machine learning (ML) algorithm. The trained ML model was able to generalize and accurately predict the ULSF on the holdout set, having a R^2 of 88.0%. Moreover, the model suggested an optimum parameter combination (RS = 2310 rpm, WT = 5.3 s and PD = 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.