Measurement and uncertainty analysis of surface roughness and material removal rate in micro turning operation and process parameters optimization
•Ra measurement and MRR determination in micro-turning operation.•Uncertainty analysis of the measured results and inference prediction.•Regressing modelling and parameters optimization using Genetic algorithm.•Optimum parameters prediction for process improvement in micro fabrication. The research...
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Veröffentlicht in: | Measurement : journal of the International Measurement Confederation 2019-07, Vol.140, p.538-547 |
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description | •Ra measurement and MRR determination in micro-turning operation.•Uncertainty analysis of the measured results and inference prediction.•Regressing modelling and parameters optimization using Genetic algorithm.•Optimum parameters prediction for process improvement in micro fabrication.
The research work dealt with measurement and analysis of the effects of cutting conditions include Spindle speed (n), Feed rate (f) and Depth of Cut (ap) on Arithmetic Average Surface Roughness (Ra) and Material Removal Rate (MRR) in micro turning operation. C360 Copper alloy and Tungsten Carbide insert are chosen as the work and tool material respectively. Taguchi L27 Orthogonal Array based Experiments were incorporated and micro pin of size 800 µm are fabricated in micromachining tool. Quality of the measured results validated with Type-‘A’ uncertainty analysis and observed a standard deviation of ±0.007 µm. It is observed that, combination of lower range of process variables (within the given range) yields fine surface finish and higher range of variables yields maximum MRR but poor surface finish. Analysis of variance was performed and inferred that, “ap” has significant influence on Ra and MRR. Also observed that, higher ‘f’ affects surface finish by marking phenomenon. Parameters optimization was carried out using Genetic Algorithm (GA). Optimum values of Ra and MRR were found as 0.031 µm and 0.0768 mm3/s respectively and their process parameters are n = 1686 rev/min, f = 10.6242 µm/rev and ap = 99.45 µm. Finally accuracy of GA results validated with confirmation experiments. |
doi_str_mv | 10.1016/j.measurement.2019.04.029 |
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The research work dealt with measurement and analysis of the effects of cutting conditions include Spindle speed (n), Feed rate (f) and Depth of Cut (ap) on Arithmetic Average Surface Roughness (Ra) and Material Removal Rate (MRR) in micro turning operation. C360 Copper alloy and Tungsten Carbide insert are chosen as the work and tool material respectively. Taguchi L27 Orthogonal Array based Experiments were incorporated and micro pin of size 800 µm are fabricated in micromachining tool. Quality of the measured results validated with Type-‘A’ uncertainty analysis and observed a standard deviation of ±0.007 µm. It is observed that, combination of lower range of process variables (within the given range) yields fine surface finish and higher range of variables yields maximum MRR but poor surface finish. Analysis of variance was performed and inferred that, “ap” has significant influence on Ra and MRR. Also observed that, higher ‘f’ affects surface finish by marking phenomenon. Parameters optimization was carried out using Genetic Algorithm (GA). Optimum values of Ra and MRR were found as 0.031 µm and 0.0768 mm3/s respectively and their process parameters are n = 1686 rev/min, f = 10.6242 µm/rev and ap = 99.45 µm. Finally accuracy of GA results validated with confirmation experiments.</description><identifier>ISSN: 0263-2241</identifier><identifier>EISSN: 1873-412X</identifier><identifier>DOI: 10.1016/j.measurement.2019.04.029</identifier><language>eng</language><publisher>London: Elsevier Ltd</publisher><subject>Alloys ; Carbide tools ; Copper ; Copper base alloys ; Cutting speed ; Feed rate ; Genetic algorithm ; Genetic algorithms ; Material removal rate ; Material removal rate (machining) ; Mathematical analysis ; Micro turning ; Micromachining ; Optimization ; Process parameters ; Process variables ; Surface finish ; Surface roughness ; Tungsten carbide ; Turning (machining) ; Uncertainty analysis ; Variance analysis</subject><ispartof>Measurement : journal of the International Measurement Confederation, 2019-07, Vol.140, p.538-547</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Jul 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-f621191a81c5cd06077c09fd72684abee2bcdc47758d7773b07a677070757f763</citedby><cites>FETCH-LOGICAL-c349t-f621191a81c5cd06077c09fd72684abee2bcdc47758d7773b07a677070757f763</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.measurement.2019.04.029$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3549,27923,27924,45994</link.rule.ids></links><search><creatorcontrib>Leo Kumar, S.P.</creatorcontrib><title>Measurement and uncertainty analysis of surface roughness and material removal rate in micro turning operation and process parameters optimization</title><title>Measurement : journal of the International Measurement Confederation</title><description>•Ra measurement and MRR determination in micro-turning operation.•Uncertainty analysis of the measured results and inference prediction.•Regressing modelling and parameters optimization using Genetic algorithm.•Optimum parameters prediction for process improvement in micro fabrication.
The research work dealt with measurement and analysis of the effects of cutting conditions include Spindle speed (n), Feed rate (f) and Depth of Cut (ap) on Arithmetic Average Surface Roughness (Ra) and Material Removal Rate (MRR) in micro turning operation. C360 Copper alloy and Tungsten Carbide insert are chosen as the work and tool material respectively. Taguchi L27 Orthogonal Array based Experiments were incorporated and micro pin of size 800 µm are fabricated in micromachining tool. Quality of the measured results validated with Type-‘A’ uncertainty analysis and observed a standard deviation of ±0.007 µm. It is observed that, combination of lower range of process variables (within the given range) yields fine surface finish and higher range of variables yields maximum MRR but poor surface finish. Analysis of variance was performed and inferred that, “ap” has significant influence on Ra and MRR. Also observed that, higher ‘f’ affects surface finish by marking phenomenon. Parameters optimization was carried out using Genetic Algorithm (GA). Optimum values of Ra and MRR were found as 0.031 µm and 0.0768 mm3/s respectively and their process parameters are n = 1686 rev/min, f = 10.6242 µm/rev and ap = 99.45 µm. Finally accuracy of GA results validated with confirmation experiments.</description><subject>Alloys</subject><subject>Carbide tools</subject><subject>Copper</subject><subject>Copper base alloys</subject><subject>Cutting speed</subject><subject>Feed rate</subject><subject>Genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Material removal rate</subject><subject>Material removal rate (machining)</subject><subject>Mathematical analysis</subject><subject>Micro turning</subject><subject>Micromachining</subject><subject>Optimization</subject><subject>Process parameters</subject><subject>Process variables</subject><subject>Surface finish</subject><subject>Surface roughness</subject><subject>Tungsten carbide</subject><subject>Turning (machining)</subject><subject>Uncertainty analysis</subject><subject>Variance analysis</subject><issn>0263-2241</issn><issn>1873-412X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqNUU1rGzEQFSWBuk7_g0rOu5W0suQ9FpO0BYdcUuhNyNpZV8YrbSStwf0Z-cUZ24X0GOYwjPTem49HyBfOas64-rqrB7B5SjBAKLVgvK2ZrJloP5AZX-qmklz8viIzJlRTCSH5R_Ip5x1jTDWtmpGXhzc6taGjU3CQivWhHLG2-2P2mcaeIqi3DmiK0_ZPgJzP6MEWSN7uKSrEwynjA_WBDt6lSMuUgg9bGkfADx_DmTSm6E4Co012ABTABmPxg_97xtyQ697uM3z-l-fk1_3d0-pHtX78_nP1bV25Rral6pXgvOV2yd3CdUwxrR1r-04LtZR2AyA2rnNS68Wy01o3G6at0pphLHSvVTMntxddnOd5glzMLuK82NLgoSSXyJWIai8o3CfnBL0Zkx9sOhrOzMkCszP_WWBOFhgmDVqA3NWFC7jGwUMy2XnAA3c-gSumi_4dKq8oJJof</recordid><startdate>201907</startdate><enddate>201907</enddate><creator>Leo Kumar, S.P.</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>201907</creationdate><title>Measurement and uncertainty analysis of surface roughness and material removal rate in micro turning operation and process parameters optimization</title><author>Leo Kumar, S.P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-f621191a81c5cd06077c09fd72684abee2bcdc47758d7773b07a677070757f763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Alloys</topic><topic>Carbide tools</topic><topic>Copper</topic><topic>Copper base alloys</topic><topic>Cutting speed</topic><topic>Feed rate</topic><topic>Genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Material removal rate</topic><topic>Material removal rate (machining)</topic><topic>Mathematical analysis</topic><topic>Micro turning</topic><topic>Micromachining</topic><topic>Optimization</topic><topic>Process parameters</topic><topic>Process variables</topic><topic>Surface finish</topic><topic>Surface roughness</topic><topic>Tungsten carbide</topic><topic>Turning (machining)</topic><topic>Uncertainty analysis</topic><topic>Variance analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Leo Kumar, S.P.</creatorcontrib><collection>CrossRef</collection><jtitle>Measurement : journal of the International Measurement Confederation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Leo Kumar, S.P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Measurement and uncertainty analysis of surface roughness and material removal rate in micro turning operation and process parameters optimization</atitle><jtitle>Measurement : journal of the International Measurement Confederation</jtitle><date>2019-07</date><risdate>2019</risdate><volume>140</volume><spage>538</spage><epage>547</epage><pages>538-547</pages><issn>0263-2241</issn><eissn>1873-412X</eissn><abstract>•Ra measurement and MRR determination in micro-turning operation.•Uncertainty analysis of the measured results and inference prediction.•Regressing modelling and parameters optimization using Genetic algorithm.•Optimum parameters prediction for process improvement in micro fabrication.
The research work dealt with measurement and analysis of the effects of cutting conditions include Spindle speed (n), Feed rate (f) and Depth of Cut (ap) on Arithmetic Average Surface Roughness (Ra) and Material Removal Rate (MRR) in micro turning operation. C360 Copper alloy and Tungsten Carbide insert are chosen as the work and tool material respectively. Taguchi L27 Orthogonal Array based Experiments were incorporated and micro pin of size 800 µm are fabricated in micromachining tool. Quality of the measured results validated with Type-‘A’ uncertainty analysis and observed a standard deviation of ±0.007 µm. It is observed that, combination of lower range of process variables (within the given range) yields fine surface finish and higher range of variables yields maximum MRR but poor surface finish. Analysis of variance was performed and inferred that, “ap” has significant influence on Ra and MRR. Also observed that, higher ‘f’ affects surface finish by marking phenomenon. Parameters optimization was carried out using Genetic Algorithm (GA). Optimum values of Ra and MRR were found as 0.031 µm and 0.0768 mm3/s respectively and their process parameters are n = 1686 rev/min, f = 10.6242 µm/rev and ap = 99.45 µm. Finally accuracy of GA results validated with confirmation experiments.</abstract><cop>London</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.measurement.2019.04.029</doi><tpages>10</tpages></addata></record> |
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subjects | Alloys Carbide tools Copper Copper base alloys Cutting speed Feed rate Genetic algorithm Genetic algorithms Material removal rate Material removal rate (machining) Mathematical analysis Micro turning Micromachining Optimization Process parameters Process variables Surface finish Surface roughness Tungsten carbide Turning (machining) Uncertainty analysis Variance analysis |
title | Measurement and uncertainty analysis of surface roughness and material removal rate in micro turning operation and process parameters optimization |
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