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Areal Surface Roughness of AZ31B Magnesium Alloy Processed by Dry Face Turning: An Experimental Framework Combined with Regression Analysis

Surface roughness is used to quantitatively evaluate the surface topography of the workpiece subjected to mechanical processing. The optimal machining parameters are critical to getting designed surface roughness. The effects of cutting speed, feed rate, and depth of cut on the areal surface roughne...

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Autores principales: Gao, Honghong, Ma, Baoji, Singh, Ravi Pratap, Yang, Heng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287581/
https://www.ncbi.nlm.nih.gov/pubmed/32429428
http://dx.doi.org/10.3390/ma13102303
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author Gao, Honghong
Ma, Baoji
Singh, Ravi Pratap
Yang, Heng
author_facet Gao, Honghong
Ma, Baoji
Singh, Ravi Pratap
Yang, Heng
author_sort Gao, Honghong
collection PubMed
description Surface roughness is used to quantitatively evaluate the surface topography of the workpiece subjected to mechanical processing. The optimal machining parameters are critical to getting designed surface roughness. The effects of cutting speed, feed rate, and depth of cut on the areal surface roughness of AZ31B Mg alloys were investigated via experiments combined with regression analysis. An orthogonal design was adopted to process the dry turning experiment of the front end face of the AZ31B bar. The areal surface roughness Sa and Sz of the end face were measured with an interferometer and analyzed through direct analysis and variance analysis (ANOVA). Then, an empirical model was established to predict the value of Sa through multiple regression analysis. Finally, a verification experiment was carried out to confirm the optimal combination of parameters for the minimum Sa and Sz, as well as the availability of the regression model for predicting Sa. The results show that both Sa and Sz of the machined end face reduce with the decrease in feed rate. The minimum of Sa and Sz reaches to 0.577 and 5.480 µm, respectively, with the cutting speed of 85 m/min, the feed rate of 0.05 mm/rev, and a depth of cut of 0.3 mm. The feed rate, depth of cut, and cutting speed contribute the greatest, the second and the smallest to Sa, respectively. The linear regression model can predict Sa of AZ31B machined with dry face turning, since the cutting speed, feed rate and depth of cut can explain 97.5% of the variation of Sa.
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spelling pubmed-72875812020-06-15 Areal Surface Roughness of AZ31B Magnesium Alloy Processed by Dry Face Turning: An Experimental Framework Combined with Regression Analysis Gao, Honghong Ma, Baoji Singh, Ravi Pratap Yang, Heng Materials (Basel) Article Surface roughness is used to quantitatively evaluate the surface topography of the workpiece subjected to mechanical processing. The optimal machining parameters are critical to getting designed surface roughness. The effects of cutting speed, feed rate, and depth of cut on the areal surface roughness of AZ31B Mg alloys were investigated via experiments combined with regression analysis. An orthogonal design was adopted to process the dry turning experiment of the front end face of the AZ31B bar. The areal surface roughness Sa and Sz of the end face were measured with an interferometer and analyzed through direct analysis and variance analysis (ANOVA). Then, an empirical model was established to predict the value of Sa through multiple regression analysis. Finally, a verification experiment was carried out to confirm the optimal combination of parameters for the minimum Sa and Sz, as well as the availability of the regression model for predicting Sa. The results show that both Sa and Sz of the machined end face reduce with the decrease in feed rate. The minimum of Sa and Sz reaches to 0.577 and 5.480 µm, respectively, with the cutting speed of 85 m/min, the feed rate of 0.05 mm/rev, and a depth of cut of 0.3 mm. The feed rate, depth of cut, and cutting speed contribute the greatest, the second and the smallest to Sa, respectively. The linear regression model can predict Sa of AZ31B machined with dry face turning, since the cutting speed, feed rate and depth of cut can explain 97.5% of the variation of Sa. MDPI 2020-05-16 /pmc/articles/PMC7287581/ /pubmed/32429428 http://dx.doi.org/10.3390/ma13102303 Text en © 2020 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
Gao, Honghong
Ma, Baoji
Singh, Ravi Pratap
Yang, Heng
Areal Surface Roughness of AZ31B Magnesium Alloy Processed by Dry Face Turning: An Experimental Framework Combined with Regression Analysis
title Areal Surface Roughness of AZ31B Magnesium Alloy Processed by Dry Face Turning: An Experimental Framework Combined with Regression Analysis
title_full Areal Surface Roughness of AZ31B Magnesium Alloy Processed by Dry Face Turning: An Experimental Framework Combined with Regression Analysis
title_fullStr Areal Surface Roughness of AZ31B Magnesium Alloy Processed by Dry Face Turning: An Experimental Framework Combined with Regression Analysis
title_full_unstemmed Areal Surface Roughness of AZ31B Magnesium Alloy Processed by Dry Face Turning: An Experimental Framework Combined with Regression Analysis
title_short Areal Surface Roughness of AZ31B Magnesium Alloy Processed by Dry Face Turning: An Experimental Framework Combined with Regression Analysis
title_sort areal surface roughness of az31b magnesium alloy processed by dry face turning: an experimental framework combined with regression analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287581/
https://www.ncbi.nlm.nih.gov/pubmed/32429428
http://dx.doi.org/10.3390/ma13102303
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