A Hybridization of Machine Learning and NSGA-II for Multi-Objective Optimization of Surface Roughness and Cutting Force in ANSI 4340 Alloy Steel Turning
This work focuses on optimizing process parameters in turning AISI 4340 alloy steel. A hybridization of Machine Learning (ML) algorithms and a Non-Dominated Sorting Genetic Algorithm (NSGA-II) is applied to find the Pareto solution. The objective functions are a simultaneous minimum of average surfa...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT
2023-02-01
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| Series: | Journal of Machine Engineering |
| Subjects: | |
| Online Access: | http://jmacheng.not.pl/A-Hybridization-of-Machine-Learning-and-NSGA-II-for-Multi-Objective-Optimization,160172,0,2.html |
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