Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition
This paper presents the multi-objective optimization of the hard milling process of AISI H13 steel under minimum quality lubricant with graphite nanoparticle. The cutting speed, feed per tooth, depth of cut, and hardness of workpiece were taken as the process parameters, while surface roughness, cut...
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| Veröffentlicht in: | Measurement and control (London) 2021-05, Vol.54 (5-6), p.820-834 |
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| creator | Vu, Ngoc-Chien Dang, Xuan-Phuong Huang, Shyh-Chour |
| description | This paper presents the multi-objective optimization of the hard milling process of AISI H13 steel under minimum quality lubricant with graphite nanoparticle. The cutting speed, feed per tooth, depth of cut, and hardness of workpiece were taken as the process parameters, while surface roughness, cutting energy, cutting temperature, and material removal rate were considered as technological responses. Response surface or Kriging approximate models were applied to generate the mathematical regression models showing the relationship between machining inputs and outputs obtained by physical experiments. Then, multi-objective particle swarm optimization algorithm in conjunction with the Pareto approach and engineering data mining was adopted to figure out the feasible solutions. The research results show that cutting energy can be reduced up to around 14% compared to the worst case. Based on the Pareto plot, the appropriate selection of machining parameters can help the machine tool operator to increase machining productivity and energy efficiency. |
| doi_str_mv | 10.1177/0020294020919457 |
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The cutting speed, feed per tooth, depth of cut, and hardness of workpiece were taken as the process parameters, while surface roughness, cutting energy, cutting temperature, and material removal rate were considered as technological responses. Response surface or Kriging approximate models were applied to generate the mathematical regression models showing the relationship between machining inputs and outputs obtained by physical experiments. Then, multi-objective particle swarm optimization algorithm in conjunction with the Pareto approach and engineering data mining was adopted to figure out the feasible solutions. The research results show that cutting energy can be reduced up to around 14% compared to the worst case. Based on the Pareto plot, the appropriate selection of machining parameters can help the machine tool operator to increase machining productivity and energy efficiency.</description><identifier>ISSN: 0020-2940</identifier><identifier>EISSN: 2051-8730</identifier><identifier>DOI: 10.1177/0020294020919457</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Algorithms ; Chromium molybdenum vanadium steels ; Cutting energy ; Cutting parameters ; Cutting speed ; Data mining ; Hot work tool steels ; Lubricants ; Lubricants & lubrication ; Machine tools ; Material removal rate (machining) ; Milling (machining) ; Multiple objective analysis ; Nanofluids ; Nanoparticles ; Optimization ; Pareto optimization ; Particle swarm optimization ; Process parameters ; Productivity ; Regression models ; Response surface methodology ; Surface roughness ; Workpieces</subject><ispartof>Measurement and control (London), 2021-05, Vol.54 (5-6), p.820-834</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. This work is licensed under the Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/ (the “License”). 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The cutting speed, feed per tooth, depth of cut, and hardness of workpiece were taken as the process parameters, while surface roughness, cutting energy, cutting temperature, and material removal rate were considered as technological responses. Response surface or Kriging approximate models were applied to generate the mathematical regression models showing the relationship between machining inputs and outputs obtained by physical experiments. Then, multi-objective particle swarm optimization algorithm in conjunction with the Pareto approach and engineering data mining was adopted to figure out the feasible solutions. The research results show that cutting energy can be reduced up to around 14% compared to the worst case. Based on the Pareto plot, the appropriate selection of machining parameters can help the machine tool operator to increase machining productivity and energy efficiency.</description><subject>Algorithms</subject><subject>Chromium molybdenum vanadium steels</subject><subject>Cutting energy</subject><subject>Cutting parameters</subject><subject>Cutting speed</subject><subject>Data mining</subject><subject>Hot work tool steels</subject><subject>Lubricants</subject><subject>Lubricants & lubrication</subject><subject>Machine tools</subject><subject>Material removal rate (machining)</subject><subject>Milling (machining)</subject><subject>Multiple objective analysis</subject><subject>Nanofluids</subject><subject>Nanoparticles</subject><subject>Optimization</subject><subject>Pareto optimization</subject><subject>Particle swarm optimization</subject><subject>Process parameters</subject><subject>Productivity</subject><subject>Regression models</subject><subject>Response surface methodology</subject><subject>Surface roughness</subject><subject>Workpieces</subject><issn>0020-2940</issn><issn>2051-8730</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>AFRWT</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNp1UU2LFDEQDaLgsLt3jwGvtiad7nRyXBZ1B3bx4N5DPscM3clsOhHG_-R_NJkWBcEcKsV79V4VVQC8weg9xtP0AaEe9XyokWM-jNMLsOvRiDs2EfQS7BrdNf41uFnXI6qPUUp7ugM_H8ucfRfV0ersv1sYT9kv_ofMPgYYHfwmk4GLn2cfDvCUorbr2vDb_dc9vMcE-gCzTcsFrLwpzcfn8zv4XOR8SWQwUJecm4UNNh3OsARjEwwyRDcX3zoEv5SlaUKuIjgXlbzextAxGN-ya_DKyXm1N7__K_D06ePT3X338OXz_u72odMDnnJHHbdcEzbSnpgRa1JhZ6hlaiSMWVlXRrE2lCBniFTOKjoN1uBR03FQilyB_WZrojyKU_KLTGcRpRcXIKaDkCl7PVthJUdaq4lpxwfJGDeKjlQRwqiTA5XV6-3mVXfzXOyaxTGWFOr0oh8pR5hMnNYqtFXpFNc1WfenK0ainVj8e-Iq6TbJKg_2r-l_638BfcqpdA</recordid><startdate>202105</startdate><enddate>202105</enddate><creator>Vu, Ngoc-Chien</creator><creator>Dang, Xuan-Phuong</creator><creator>Huang, Shyh-Chour</creator><general>SAGE Publications</general><general>Sage Publications Ltd</general><general>SAGE Publishing</general><scope>AFRWT</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>L7M</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4069-2369</orcidid><orcidid>https://orcid.org/0000-0002-7158-3720</orcidid></search><sort><creationdate>202105</creationdate><title>Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition</title><author>Vu, Ngoc-Chien ; Dang, Xuan-Phuong ; Huang, Shyh-Chour</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c417t-6f9e9c385623d51c3c41fd6e8b5388ea17761cd630fd3abfeb674ed15c654bb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Chromium molybdenum vanadium steels</topic><topic>Cutting energy</topic><topic>Cutting parameters</topic><topic>Cutting speed</topic><topic>Data mining</topic><topic>Hot work tool steels</topic><topic>Lubricants</topic><topic>Lubricants & lubrication</topic><topic>Machine tools</topic><topic>Material removal rate (machining)</topic><topic>Milling (machining)</topic><topic>Multiple objective analysis</topic><topic>Nanofluids</topic><topic>Nanoparticles</topic><topic>Optimization</topic><topic>Pareto optimization</topic><topic>Particle swarm optimization</topic><topic>Process parameters</topic><topic>Productivity</topic><topic>Regression models</topic><topic>Response surface methodology</topic><topic>Surface roughness</topic><topic>Workpieces</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vu, Ngoc-Chien</creatorcontrib><creatorcontrib>Dang, Xuan-Phuong</creatorcontrib><creatorcontrib>Huang, Shyh-Chour</creatorcontrib><collection>Sage Journals GOLD Open Access 2024</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Measurement and control (London)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vu, Ngoc-Chien</au><au>Dang, Xuan-Phuong</au><au>Huang, Shyh-Chour</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition</atitle><jtitle>Measurement and control (London)</jtitle><date>2021-05</date><risdate>2021</risdate><volume>54</volume><issue>5-6</issue><spage>820</spage><epage>834</epage><pages>820-834</pages><issn>0020-2940</issn><eissn>2051-8730</eissn><abstract>This paper presents the multi-objective optimization of the hard milling process of AISI H13 steel under minimum quality lubricant with graphite nanoparticle. The cutting speed, feed per tooth, depth of cut, and hardness of workpiece were taken as the process parameters, while surface roughness, cutting energy, cutting temperature, and material removal rate were considered as technological responses. Response surface or Kriging approximate models were applied to generate the mathematical regression models showing the relationship between machining inputs and outputs obtained by physical experiments. Then, multi-objective particle swarm optimization algorithm in conjunction with the Pareto approach and engineering data mining was adopted to figure out the feasible solutions. The research results show that cutting energy can be reduced up to around 14% compared to the worst case. Based on the Pareto plot, the appropriate selection of machining parameters can help the machine tool operator to increase machining productivity and energy efficiency.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><doi>10.1177/0020294020919457</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-4069-2369</orcidid><orcidid>https://orcid.org/0000-0002-7158-3720</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Chromium molybdenum vanadium steels Cutting energy Cutting parameters Cutting speed Data mining Hot work tool steels Lubricants Lubricants & lubrication Machine tools Material removal rate (machining) Milling (machining) Multiple objective analysis Nanofluids Nanoparticles Optimization Pareto optimization Particle swarm optimization Process parameters Productivity Regression models Response surface methodology Surface roughness Workpieces |
| title | Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition |
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