A comparative study of basic and ensemble artificial intelligence models for surface roughness prediction during the AA7075 milling process
This study reports on how ML algorithms are employed to investigate and predict surface roughness. Experiments were executed with a CNC milling machine, using AA7075 as part material and a “TiCN” coated tool. Feed rates per tooth, cutting speeds, cut depth, and cutting fluid were studied in response...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2023-05, Vol.126 (1-2), p.1-15 |
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| creator | Gabsi, Abd El Hedi Ben Aissa, Chokri Mathlouthi, Safa |
| description | This study reports on how ML algorithms are employed to investigate and predict surface roughness. Experiments were executed with a CNC milling machine, using AA7075 as part material and a “TiCN” coated tool. Feed rates per tooth, cutting speeds, cut depth, and cutting fluid were studied in response to roughness average (Ra) values. In the present study, Ra was measured with contact stylus tracing. Forty-two experiments were executed: thirty-three were used in all models training and nine in tests, and an additional experiment was carried out with diverse cutting parameters to validate the preferred models. This is the first study where thirteen ML algorithms, of which seven are basic and six are ensemble models, have been studied in the context of surface roughness. The study results showed that the voting regression model was the best model according to performance metrics (
R
2
= 0.97, RAE = 0.17, RMSE = 0.0325, MAE = 0.13, and RSE = 0.09) and deviation 5.66%. Manufacturing companies can employ the voting regression model to predict surface roughness to enhance manufacturing efficiency, by harmonizing cutting conditions values against surface roughness. |
| doi_str_mv | 10.1007/s00170-023-11026-8 |
| format | Article |
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R
2
= 0.97, RAE = 0.17, RMSE = 0.0325, MAE = 0.13, and RSE = 0.09) and deviation 5.66%. Manufacturing companies can employ the voting regression model to predict surface roughness to enhance manufacturing efficiency, by harmonizing cutting conditions values against surface roughness.</description><identifier>ISSN: 0268-3768</identifier><identifier>EISSN: 1433-3015</identifier><identifier>DOI: 10.1007/s00170-023-11026-8</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Algorithms ; Artificial intelligence ; CAE) and Design ; Comparative studies ; Computer-Aided Engineering (CAD ; Cutting fluids ; Cutting parameters ; Cutting speed ; Engineering ; Feed rate ; Industrial and Production Engineering ; Manufacturing ; Mechanical Engineering ; Media Management ; Milling machines ; Original Article ; Performance measurement ; Regression models ; Styli ; Surface roughness ; Titanium carbonitride</subject><ispartof>International journal of advanced manufacturing technology, 2023-05, Vol.126 (1-2), p.1-15</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-65d0d237d0222444fffa9faf0045b83fca5a9161b38b4f5f7bd0a87c3086c9093</citedby><cites>FETCH-LOGICAL-c319t-65d0d237d0222444fffa9faf0045b83fca5a9161b38b4f5f7bd0a87c3086c9093</cites><orcidid>0000-0003-4925-2367</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00170-023-11026-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00170-023-11026-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids></links><search><creatorcontrib>Gabsi, Abd El Hedi</creatorcontrib><creatorcontrib>Ben Aissa, Chokri</creatorcontrib><creatorcontrib>Mathlouthi, Safa</creatorcontrib><title>A comparative study of basic and ensemble artificial intelligence models for surface roughness prediction during the AA7075 milling process</title><title>International journal of advanced manufacturing technology</title><addtitle>Int J Adv Manuf Technol</addtitle><description>This study reports on how ML algorithms are employed to investigate and predict surface roughness. Experiments were executed with a CNC milling machine, using AA7075 as part material and a “TiCN” coated tool. Feed rates per tooth, cutting speeds, cut depth, and cutting fluid were studied in response to roughness average (Ra) values. In the present study, Ra was measured with contact stylus tracing. Forty-two experiments were executed: thirty-three were used in all models training and nine in tests, and an additional experiment was carried out with diverse cutting parameters to validate the preferred models. This is the first study where thirteen ML algorithms, of which seven are basic and six are ensemble models, have been studied in the context of surface roughness. The study results showed that the voting regression model was the best model according to performance metrics (
R
2
= 0.97, RAE = 0.17, RMSE = 0.0325, MAE = 0.13, and RSE = 0.09) and deviation 5.66%. Manufacturing companies can employ the voting regression model to predict surface roughness to enhance manufacturing efficiency, by harmonizing cutting conditions values against surface roughness.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>CAE) and Design</subject><subject>Comparative studies</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Cutting fluids</subject><subject>Cutting parameters</subject><subject>Cutting speed</subject><subject>Engineering</subject><subject>Feed rate</subject><subject>Industrial and Production Engineering</subject><subject>Manufacturing</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Milling machines</subject><subject>Original Article</subject><subject>Performance measurement</subject><subject>Regression models</subject><subject>Styli</subject><subject>Surface roughness</subject><subject>Titanium carbonitride</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kMtKxDAUhoMoOI6-gKuA6-hJ00u6LIM3ENzoOqS5zGRom5q0wjyDL23GCu5cHfj5L5wPoWsKtxSguosAtAICGSOUQlYSfoJWNGeMMKDFKVoljRNWlfwcXcS4T_aSlnyFvhqsfD_KICf3aXCcZn3A3uJWRqewHDQ2QzR92xksw-SsU0522A2T6Tq3NYMyuPfadBFbH3Ccg5VJCn7e7gYTIx6D0U5Nzg9Yz8ENWzztDG6aCqoC9y6VJGkMXiXzJTqzsovm6veu0fvD_dvmiby8Pj5vmheiGK0nUhYadMYqDVmW5XlurZW1lRYgL1rOrJKFrNN7LeNtbgtbtRokrxQDXqoaarZGN0tv2v2YTZzE3s9hSJMi45DwUVoeXdniUsHHGIwVY3C9DAdBQRyhiwW6SNDFD3TBU4gtoTgenzXhr_qf1DdyToZw</recordid><startdate>20230501</startdate><enddate>20230501</enddate><creator>Gabsi, Abd El Hedi</creator><creator>Ben Aissa, Chokri</creator><creator>Mathlouthi, Safa</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0003-4925-2367</orcidid></search><sort><creationdate>20230501</creationdate><title>A comparative study of basic and ensemble artificial intelligence models for surface roughness prediction during the AA7075 milling process</title><author>Gabsi, Abd El Hedi ; Ben Aissa, Chokri ; Mathlouthi, Safa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-65d0d237d0222444fffa9faf0045b83fca5a9161b38b4f5f7bd0a87c3086c9093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>CAE) and Design</topic><topic>Comparative studies</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Cutting fluids</topic><topic>Cutting parameters</topic><topic>Cutting speed</topic><topic>Engineering</topic><topic>Feed rate</topic><topic>Industrial and Production Engineering</topic><topic>Manufacturing</topic><topic>Mechanical Engineering</topic><topic>Media Management</topic><topic>Milling machines</topic><topic>Original Article</topic><topic>Performance measurement</topic><topic>Regression models</topic><topic>Styli</topic><topic>Surface roughness</topic><topic>Titanium carbonitride</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gabsi, Abd El Hedi</creatorcontrib><creatorcontrib>Ben Aissa, Chokri</creatorcontrib><creatorcontrib>Mathlouthi, Safa</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering 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>Engineering Collection</collection><jtitle>International journal of advanced manufacturing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gabsi, Abd El Hedi</au><au>Ben Aissa, Chokri</au><au>Mathlouthi, Safa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comparative study of basic and ensemble artificial intelligence models for surface roughness prediction during the AA7075 milling process</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2023-05-01</date><risdate>2023</risdate><volume>126</volume><issue>1-2</issue><spage>1</spage><epage>15</epage><pages>1-15</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>This study reports on how ML algorithms are employed to investigate and predict surface roughness. Experiments were executed with a CNC milling machine, using AA7075 as part material and a “TiCN” coated tool. Feed rates per tooth, cutting speeds, cut depth, and cutting fluid were studied in response to roughness average (Ra) values. In the present study, Ra was measured with contact stylus tracing. Forty-two experiments were executed: thirty-three were used in all models training and nine in tests, and an additional experiment was carried out with diverse cutting parameters to validate the preferred models. This is the first study where thirteen ML algorithms, of which seven are basic and six are ensemble models, have been studied in the context of surface roughness. The study results showed that the voting regression model was the best model according to performance metrics (
R
2
= 0.97, RAE = 0.17, RMSE = 0.0325, MAE = 0.13, and RSE = 0.09) and deviation 5.66%. Manufacturing companies can employ the voting regression model to predict surface roughness to enhance manufacturing efficiency, by harmonizing cutting conditions values against surface roughness.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00170-023-11026-8</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0003-4925-2367</orcidid></addata></record> |
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| subjects | Algorithms Artificial intelligence CAE) and Design Comparative studies Computer-Aided Engineering (CAD Cutting fluids Cutting parameters Cutting speed Engineering Feed rate Industrial and Production Engineering Manufacturing Mechanical Engineering Media Management Milling machines Original Article Performance measurement Regression models Styli Surface roughness Titanium carbonitride |
| title | A comparative study of basic and ensemble artificial intelligence models for surface roughness prediction during the AA7075 milling process |
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