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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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Bibliographic Details
Main Authors: Anh-Tu Nguyen, Van-Hai Nguyen, Tien-Thinh Le
Format: Bài trích
Language:English
Published: Hindawi 2022
Subjects:
Multiobjective Optimization
NSGA-II
Online Access: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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spelling 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
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score 8.891695

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