Parameter Optimization of Milling Process for Surface Roughness Constraints
In the milling process of 6061 aluminum considering the requirement of controlling the surface roughness of workpiece, artificially selected milling parameters may be conservative, resulting in low material removal rate and high manufacturing cost.Taking the surface roughness as the constraint co...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2023-02-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2173 |
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| _version_ | 1850095780565614592 |
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| author | GUO Bin YUE Caixu ZHANG Anshan JIANG Zhipeng YUE Daxun QIN Yiyuan |
| author_facet | GUO Bin YUE Caixu ZHANG Anshan JIANG Zhipeng YUE Daxun QIN Yiyuan |
| author_sort | GUO Bin |
| collection | DOAJ |
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In the milling process of 6061 aluminum considering the requirement of controlling the surface roughness of workpiece, artificially selected milling parameters may be conservative, resulting in low material removal rate and high manufacturing cost.Taking the surface roughness as the constraint condition and the maximum material removal rate as the goal, the surface roughness regression model is established based on extreme gradient boosting (XGBOOST) with the spindle speed, feed speed and cutting depth as the optimization objects.The milling parameters of spindle speed, feed speed and cutting depth are optimized by genetic algorithm.The optimal milling parameters are obtained by using the multi objective optimization characteristics of genetic algorithm.It can be seen from the four groups of optimization results that the maximum change of surface roughness is only 0.048μm, while the minimum material removal rate increases by 2458.048mm3/min.While achieving surface roughness, the processing efficiency is improved, and the manufacturing costs are reduced, resulting in good optimization effects, which has a certain guiding role in the actual processing. |
| format | Article |
| id | doaj-art-c036a65cd3c84f11b3cf862df2ce89cc |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2023-02-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-c036a65cd3c84f11b3cf862df2ce89cc2025-08-20T02:41:23ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832023-02-012801202810.15938/j.jhust.2023.01.003Parameter Optimization of Milling Process for Surface Roughness ConstraintsGUO Bin0YUE Caixu1ZHANG Anshan2JIANG Zhipeng3YUE Daxun4QIN Yiyuan5Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China In the milling process of 6061 aluminum considering the requirement of controlling the surface roughness of workpiece, artificially selected milling parameters may be conservative, resulting in low material removal rate and high manufacturing cost.Taking the surface roughness as the constraint condition and the maximum material removal rate as the goal, the surface roughness regression model is established based on extreme gradient boosting (XGBOOST) with the spindle speed, feed speed and cutting depth as the optimization objects.The milling parameters of spindle speed, feed speed and cutting depth are optimized by genetic algorithm.The optimal milling parameters are obtained by using the multi objective optimization characteristics of genetic algorithm.It can be seen from the four groups of optimization results that the maximum change of surface roughness is only 0.048μm, while the minimum material removal rate increases by 2458.048mm3/min.While achieving surface roughness, the processing efficiency is improved, and the manufacturing costs are reduced, resulting in good optimization effects, which has a certain guiding role in the actual processing.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2173millingsurface roughnessmaterial removal rategenetic algorithmparameter optimization |
| spellingShingle | GUO Bin YUE Caixu ZHANG Anshan JIANG Zhipeng YUE Daxun QIN Yiyuan Parameter Optimization of Milling Process for Surface Roughness Constraints Journal of Harbin University of Science and Technology milling surface roughness material removal rate genetic algorithm parameter optimization |
| title | Parameter Optimization of Milling Process for Surface Roughness Constraints |
| title_full | Parameter Optimization of Milling Process for Surface Roughness Constraints |
| title_fullStr | Parameter Optimization of Milling Process for Surface Roughness Constraints |
| title_full_unstemmed | Parameter Optimization of Milling Process for Surface Roughness Constraints |
| title_short | Parameter Optimization of Milling Process for Surface Roughness Constraints |
| title_sort | parameter optimization of milling process for surface roughness constraints |
| topic | milling surface roughness material removal rate genetic algorithm parameter optimization |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2173 |
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