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

Pedro Effertz*, Willian Sales de Carvalho, Rafael Paiotti Marcondes Guimaraes, G. Saria, S. T. Amancio-Filho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 R2 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.
Original languageEnglish
Article number864187
Number of pages9
JournalFrontiers in Materials
Publication statusPublished - 8 Apr 2022


  • AA2024-T3
  • machine learning
  • optimization
  • polynomial regression
  • refill friction stir spot welding

ASJC Scopus subject areas

  • Materials Science (miscellaneous)

Fields of Expertise

  • Advanced Materials Science


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