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Multi-Response Optimization of Face Milling Performance Considering Tool Path Strategies in Machining of Al-2024

It is hypothesized that the orientation of tool maneuvering in the milling process defines the quality of machining. In that respect, here, the influence of different path strategies of the tool in face milling is investigated, and subsequently, the best strategy is identified following systematic o...

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Autores principales: Ali, Raneen Abd, Mia, Mozammel, Khan, Aqib Mashood, Chen, Wenliang, Gupta, Munish Kumar, Pruncu, Catalin Iulian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479395/
https://www.ncbi.nlm.nih.gov/pubmed/30934735
http://dx.doi.org/10.3390/ma12071013
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author Ali, Raneen Abd
Mia, Mozammel
Khan, Aqib Mashood
Chen, Wenliang
Gupta, Munish Kumar
Pruncu, Catalin Iulian
author_facet Ali, Raneen Abd
Mia, Mozammel
Khan, Aqib Mashood
Chen, Wenliang
Gupta, Munish Kumar
Pruncu, Catalin Iulian
author_sort Ali, Raneen Abd
collection PubMed
description It is hypothesized that the orientation of tool maneuvering in the milling process defines the quality of machining. In that respect, here, the influence of different path strategies of the tool in face milling is investigated, and subsequently, the best strategy is identified following systematic optimization. The surface roughness, material removal rate and cutting time are considered as key responses, whereas the cutting speed, feed rate and depth of cut were considered as inputs (quantitative factors) beside the tool path strategy (qualitative factor) for the material Al 2024 with a torus end mill. The experimental plan, i.e., 27 runs were determined by using the Taguchi design approach. In addition, the analysis of variance is conducted to statistically identify the effects of parameters. The optimal values of process parameters have been evaluated based on Taguchi-grey relational analysis, and the reliability of this analysis has been verified with the confirmation test. It was found that the tool path strategy has a significant influence on the end outcomes of face milling. As such, the surface topography respective to different cutter path strategies and the optimal cutting strategy is discussed in detail.
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spelling pubmed-64793952019-04-29 Multi-Response Optimization of Face Milling Performance Considering Tool Path Strategies in Machining of Al-2024 Ali, Raneen Abd Mia, Mozammel Khan, Aqib Mashood Chen, Wenliang Gupta, Munish Kumar Pruncu, Catalin Iulian Materials (Basel) Article It is hypothesized that the orientation of tool maneuvering in the milling process defines the quality of machining. In that respect, here, the influence of different path strategies of the tool in face milling is investigated, and subsequently, the best strategy is identified following systematic optimization. The surface roughness, material removal rate and cutting time are considered as key responses, whereas the cutting speed, feed rate and depth of cut were considered as inputs (quantitative factors) beside the tool path strategy (qualitative factor) for the material Al 2024 with a torus end mill. The experimental plan, i.e., 27 runs were determined by using the Taguchi design approach. In addition, the analysis of variance is conducted to statistically identify the effects of parameters. The optimal values of process parameters have been evaluated based on Taguchi-grey relational analysis, and the reliability of this analysis has been verified with the confirmation test. It was found that the tool path strategy has a significant influence on the end outcomes of face milling. As such, the surface topography respective to different cutter path strategies and the optimal cutting strategy is discussed in detail. MDPI 2019-03-27 /pmc/articles/PMC6479395/ /pubmed/30934735 http://dx.doi.org/10.3390/ma12071013 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ali, Raneen Abd
Mia, Mozammel
Khan, Aqib Mashood
Chen, Wenliang
Gupta, Munish Kumar
Pruncu, Catalin Iulian
Multi-Response Optimization of Face Milling Performance Considering Tool Path Strategies in Machining of Al-2024
title Multi-Response Optimization of Face Milling Performance Considering Tool Path Strategies in Machining of Al-2024
title_full Multi-Response Optimization of Face Milling Performance Considering Tool Path Strategies in Machining of Al-2024
title_fullStr Multi-Response Optimization of Face Milling Performance Considering Tool Path Strategies in Machining of Al-2024
title_full_unstemmed Multi-Response Optimization of Face Milling Performance Considering Tool Path Strategies in Machining of Al-2024
title_short Multi-Response Optimization of Face Milling Performance Considering Tool Path Strategies in Machining of Al-2024
title_sort multi-response optimization of face milling performance considering tool path strategies in machining of al-2024
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479395/
https://www.ncbi.nlm.nih.gov/pubmed/30934735
http://dx.doi.org/10.3390/ma12071013
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