Optimization of Refill Friction Stir Spot Welded AA2024-T3 Using Machine Learning
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 mechanica...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Materials |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2022.864187/full |
| _version_ | 1818366516863696896 |
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| author | P. S. Effertz W. S. de Carvalho R. P. M. Guimarães G. Saria S. T. Amancio-Filho |
| author_facet | P. S. Effertz W. S. de Carvalho R. P. M. Guimarães G. Saria S. T. Amancio-Filho |
| author_sort | P. S. Effertz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T22:37:24Z |
| format | Article |
| id | doaj.art-20d214aaa4d14389b967a8d050c08da1 |
| institution | Directory Open Access Journal |
| issn | 2296-8016 |
| language | English |
| last_indexed | 2024-12-13T22:37:24Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj.art-20d214aaa4d14389b967a8d050c08da12022-12-21T23:28:56ZengFrontiers Media S.A.Frontiers in Materials2296-80162022-04-01910.3389/fmats.2022.864187864187Optimization of Refill Friction Stir Spot Welded AA2024-T3 Using Machine LearningP. S. EffertzW. S. de CarvalhoR. P. M. GuimarãesG. SariaS. T. Amancio-FilhoThe 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.https://www.frontiersin.org/articles/10.3389/fmats.2022.864187/fullrefill friction stir spot weldingoptimizationmachine learningpolynomial regressionAA2024-T3 |
| spellingShingle | P. S. Effertz W. S. de Carvalho R. P. M. Guimarães G. Saria S. T. Amancio-Filho Optimization of Refill Friction Stir Spot Welded AA2024-T3 Using Machine Learning Frontiers in Materials refill friction stir spot welding optimization machine learning polynomial regression AA2024-T3 |
| title | Optimization of Refill Friction Stir Spot Welded AA2024-T3 Using Machine Learning |
| title_full | Optimization of Refill Friction Stir Spot Welded AA2024-T3 Using Machine Learning |
| title_fullStr | Optimization of Refill Friction Stir Spot Welded AA2024-T3 Using Machine Learning |
| title_full_unstemmed | Optimization of Refill Friction Stir Spot Welded AA2024-T3 Using Machine Learning |
| title_short | Optimization of Refill Friction Stir Spot Welded AA2024-T3 Using Machine Learning |
| title_sort | optimization of refill friction stir spot welded aa2024 t3 using machine learning |
| topic | refill friction stir spot welding optimization machine learning polynomial regression AA2024-T3 |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2022.864187/full |
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