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Predictive Analytical Modeling of Thermo-Mechanical Effects in Orthogonal Machining

Factor relationships in a machining system do not work in pairs. Varying the cutting parameters, materials machined, or volumes produced will influence many machining characteristics. For this reason, we are attempting to better understand the effect of the Johnson-Cook (J-C) law of behavior on cutt...

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Autores principales: Mohamed-Amine, Alliche, Mohamed, Djennane, Abdelhakim, Djebara, Victor, Songmene
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706555/
https://www.ncbi.nlm.nih.gov/pubmed/34947469
http://dx.doi.org/10.3390/ma14247876
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author Mohamed-Amine, Alliche
Mohamed, Djennane
Abdelhakim, Djebara
Victor, Songmene
author_facet Mohamed-Amine, Alliche
Mohamed, Djennane
Abdelhakim, Djebara
Victor, Songmene
author_sort Mohamed-Amine, Alliche
collection PubMed
description Factor relationships in a machining system do not work in pairs. Varying the cutting parameters, materials machined, or volumes produced will influence many machining characteristics. For this reason, we are attempting to better understand the effect of the Johnson-Cook (J-C) law of behavior on cutting temperature prediction. Thus, the objective of the present study is to investigate, experimentally and theoretically, the tool/material interactions and their effects on dust emission during orthogonal cutting. The proposed approach is built on three steps. First, we established an experimental design to analyze, experimentally, the cutting conditions effects on the cutting temperature under dry condition. The empirical model which is based on the response surface methodology was used to generate a large amount of data depending on the machining conditions. Through this step, we were able to analyze the sensitivity of the cutting temperature to different cutting parameters. It was found that cutting speed, tool tip radius, rake angle, and the interaction between the cutting speed and the rake angle explain more than 84.66% of the cutting temperature variation. The cutting temperature will be considered as a reference to validate the analytical model. Hence, a temperature prediction model is important as a second step. The modeling of orthogonal machining using the J-C plasticity model showed a good correlation between the predicted cutting temperature and that obtained by the proposed empirical model. The calculated deviations for the different cutting conditions tested are relatively acceptable (with a less than 10% error). Finally, the established analytical model was then applied to the machining processes in order to optimize the cutting parameters and, at the same time, minimize the generated dust. The evaluation of the dust generation revealed that the dust emission is closely related to the variation of the cutting temperature. We also noticed that the dust generation can indicate different phenomena of fine and ultrafine particles generation during the cutting process, related to the heat source or temperature during orthogonal machining. Finally, the effective strategy to limit dust emissions at the source is to avoid the critical temperature zone. For this purpose, the two-sided values can be seen as combinations to limit dust emissions at the source.
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spelling pubmed-87065552021-12-25 Predictive Analytical Modeling of Thermo-Mechanical Effects in Orthogonal Machining Mohamed-Amine, Alliche Mohamed, Djennane Abdelhakim, Djebara Victor, Songmene Materials (Basel) Article Factor relationships in a machining system do not work in pairs. Varying the cutting parameters, materials machined, or volumes produced will influence many machining characteristics. For this reason, we are attempting to better understand the effect of the Johnson-Cook (J-C) law of behavior on cutting temperature prediction. Thus, the objective of the present study is to investigate, experimentally and theoretically, the tool/material interactions and their effects on dust emission during orthogonal cutting. The proposed approach is built on three steps. First, we established an experimental design to analyze, experimentally, the cutting conditions effects on the cutting temperature under dry condition. The empirical model which is based on the response surface methodology was used to generate a large amount of data depending on the machining conditions. Through this step, we were able to analyze the sensitivity of the cutting temperature to different cutting parameters. It was found that cutting speed, tool tip radius, rake angle, and the interaction between the cutting speed and the rake angle explain more than 84.66% of the cutting temperature variation. The cutting temperature will be considered as a reference to validate the analytical model. Hence, a temperature prediction model is important as a second step. The modeling of orthogonal machining using the J-C plasticity model showed a good correlation between the predicted cutting temperature and that obtained by the proposed empirical model. The calculated deviations for the different cutting conditions tested are relatively acceptable (with a less than 10% error). Finally, the established analytical model was then applied to the machining processes in order to optimize the cutting parameters and, at the same time, minimize the generated dust. The evaluation of the dust generation revealed that the dust emission is closely related to the variation of the cutting temperature. We also noticed that the dust generation can indicate different phenomena of fine and ultrafine particles generation during the cutting process, related to the heat source or temperature during orthogonal machining. Finally, the effective strategy to limit dust emissions at the source is to avoid the critical temperature zone. For this purpose, the two-sided values can be seen as combinations to limit dust emissions at the source. MDPI 2021-12-19 /pmc/articles/PMC8706555/ /pubmed/34947469 http://dx.doi.org/10.3390/ma14247876 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mohamed-Amine, Alliche
Mohamed, Djennane
Abdelhakim, Djebara
Victor, Songmene
Predictive Analytical Modeling of Thermo-Mechanical Effects in Orthogonal Machining
title Predictive Analytical Modeling of Thermo-Mechanical Effects in Orthogonal Machining
title_full Predictive Analytical Modeling of Thermo-Mechanical Effects in Orthogonal Machining
title_fullStr Predictive Analytical Modeling of Thermo-Mechanical Effects in Orthogonal Machining
title_full_unstemmed Predictive Analytical Modeling of Thermo-Mechanical Effects in Orthogonal Machining
title_short Predictive Analytical Modeling of Thermo-Mechanical Effects in Orthogonal Machining
title_sort predictive analytical modeling of thermo-mechanical effects in orthogonal machining
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706555/
https://www.ncbi.nlm.nih.gov/pubmed/34947469
http://dx.doi.org/10.3390/ma14247876
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