Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
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Article Number | 01249 | |
Number of page(s) | 15 | |
DOI | https://doi.org/10.1051/e3sconf/202343001249 | |
Published online | 06 October 2023 |
Performance Evaluation of ML-Based Algorithm and Taguchi Algorithm of the Hardness Value of the Friction Stir Welded AA6262 Joints at a Nugget Joint
1 Department of Mechanical Engineering, Balaji institute of Technology and Sciences, Narsampet, Telangana. INDIA
2 Department of Mechanical Engineering, Sree Chaitanya College of Engineering, Karimnagar, Telangana 505527 India
3 Bahir Dar Institute of Technology, Faculty of Mechanical and Industrial Engineering, Bahir Dar, 6000, Ethiopia
4 Department of Chemistry, Materials, and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Milan, 20019. Italy
5 Department of Mechanical Engineering, Sree Chaitanya College of Engineering, Karimnagar, Telangana. INDIA
6 Department of Mechanical Engineering, Balaji institute of Technology and Sciences, Narsampet, Telangana. INDIA
* Corresponding author: ngk310@gmail.com
Nowadays, industry 4.0 plays a tremendous role in the manufacturing industries for increasing the amount of data and accuracy in modern manufacturing systems. Thanks to artificial intelligence, particularly machine learning, big data analytics have dramatically amended, and manufacturers easily exploit organized and unorganized data. This study utilized hybrid optimization algorithms to find friction stir welding and optimal hardness value at the nugget zone. A similar AA 6262 material was used and welded in a butt joint configuration. Tool rotational speed (RPM), tool traverse speed (mm/min), and the plane depth (mm) are used as controllable parameters and optimized using Taguchi L9, Random Forest, and XG Boost machine learning tools. Analysis of variance was also conducted at a 95% confidence interval for identifying the significant parameters. The result indicated that the coefficient of determination from Taguchi L9 orthogonal array is 0.91 obtained while Random Forest and XG Boost algorithm imparted 0.62 and 0.65 respectively. Keywords: Friction Stir Welding; Taguchi; Machine Learning; Hardness; Nugget Zone and Random Forest.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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