Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II
This work addresses the prediction and optimization of average surface roughness (Ra) and maximum flank wear (Vbmax) of 6061 aluminum alloy during high-speed milling. The investigation was done using a DMU 50 CNC 5-axis machine with Ultracut FX 6090 fluid. Four factors were examined: the table feed...
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Hindawi
2022
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| Truy cập trực tuyến: | https://www.hindawi.com/journals/amse/2022/5406570/ https://dlib.phenikaa-uni.edu.vn/handle/PNK/5977 https://doi.org/10.1155/2022/5406570 |
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oai:localhost:PNK-59772022-08-17T05:54:51Z Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II Anh-Tu Nguyen Van-Hai Nguyen Tien-Thinh Le Multiobjective Optimization NSGA-II This work addresses the prediction and optimization of average surface roughness (Ra) and maximum flank wear (Vbmax) of 6061 aluminum alloy during high-speed milling. The investigation was done using a DMU 50 CNC 5-axis machine with Ultracut FX 6090 fluid. Four factors were examined: the table feed rate, cutting speed, depth of cut, and cutting length. Three levels of each factor were examined to conduct 81 experiment runs. The response parameters in these experiments were measurements of Ra and Vbmax. 2022-07-13T02:00:12Z 2022-07-13T02:00:12Z 2022 Bài trích https://www.hindawi.com/journals/amse/2022/5406570/ https://dlib.phenikaa-uni.edu.vn/handle/PNK/5977 https://doi.org/10.1155/2022/5406570 en Hindawi |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
Multiobjective Optimization NSGA-II |
| spellingShingle |
Multiobjective Optimization NSGA-II Anh-Tu Nguyen Van-Hai Nguyen Tien-Thinh Le Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II |
| description |
This work addresses the prediction and optimization of average surface roughness (Ra) and maximum flank wear (Vbmax) of 6061 aluminum alloy during high-speed milling. The investigation was done using a DMU 50 CNC 5-axis machine with Ultracut FX 6090 fluid. Four factors were examined: the table feed rate, cutting speed, depth of cut, and cutting length. Three levels of each factor were examined to conduct 81 experiment runs. The response parameters in these experiments were measurements of Ra and Vbmax. |
| format |
Bài trích |
| author |
Anh-Tu Nguyen Van-Hai Nguyen Tien-Thinh Le |
| author_facet |
Anh-Tu Nguyen Van-Hai Nguyen Tien-Thinh Le |
| author_sort |
Anh-Tu Nguyen |
| title |
Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II |
| title_short |
Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II |
| title_full |
Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II |
| title_fullStr |
Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II |
| title_full_unstemmed |
Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II |
| title_sort |
multiobjective optimization of surface roughness and tool wear in high-speed milling of aa6061 by machine learning and nsga-ii |
| publisher |
Hindawi |
| publishDate |
2022 |
| url |
https://www.hindawi.com/journals/amse/2022/5406570/ https://dlib.phenikaa-uni.edu.vn/handle/PNK/5977 https://doi.org/10.1155/2022/5406570 |
| _version_ |
1751856291183067136 |
| score |
8.887929 |