Surface roughness modeling using RSM for HSLA steel by coated carbide tools
Surface roughness is the main indicator of surface quality on machined parts. Accurate predictive models for surface roughness help to choose optimum machining parameters, ultimately support to maximize the productivity without any compromise on quality. In this paper, an average surface roughness (...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2015-05, Vol.78 (5-8), p.1031-1041 |
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| container_title | International journal of advanced manufacturing technology |
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| creator | Azam, Muhammad Jahanzaib, Mirza Wasim, Ahmad Hussain, Salman |
| description | Surface roughness is the main indicator of surface quality on machined parts. Accurate predictive models for surface roughness help to choose optimum machining parameters, ultimately support to maximize the productivity without any compromise on quality. In this paper, an average surface roughness (
R
a
) model has been developed for turning high-strength low-alloy steel (AISI 4340 with carbon contents less than 0.3 %) using multilayer coated carbide tools. A series of tests using response surface methodology (RSM) has been employed to develop a relationship between
R
a
and machining parameters (feed, speed, and depth of cut). The feed rate has been observed as the main parameter that influences surface roughness. Contour plots of “feed versus speed” and “feed versus depth of cut” signify that target
R
a
value can be achieved through optimal combination of cutting parameters. The accuracy of proposed model has been confirmed through validation data with average prediction error of 3.38 %. |
| doi_str_mv | 10.1007/s00170-014-6707-5 |
| format | Article |
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R
a
) model has been developed for turning high-strength low-alloy steel (AISI 4340 with carbon contents less than 0.3 %) using multilayer coated carbide tools. A series of tests using response surface methodology (RSM) has been employed to develop a relationship between
R
a
and machining parameters (feed, speed, and depth of cut). The feed rate has been observed as the main parameter that influences surface roughness. Contour plots of “feed versus speed” and “feed versus depth of cut” signify that target
R
a
value can be achieved through optimal combination of cutting parameters. The accuracy of proposed model has been confirmed through validation data with average prediction error of 3.38 %.</description><identifier>ISSN: 0268-3768</identifier><identifier>EISSN: 1433-3015</identifier><identifier>DOI: 10.1007/s00170-014-6707-5</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>CAE) and Design ; Carbide tools ; Computer-Aided Engineering (CAD ; Cutting parameters ; Engineering ; Feed rate ; High strength low alloy steels ; Industrial and Production Engineering ; Mechanical Engineering ; Media Management ; Model accuracy ; Multilayers ; Optimization ; Original Article ; Prediction models ; Production planning ; Response surface methodology ; Surface properties ; Surface roughness ; Test procedures ; Tool steels ; Turning (machining)</subject><ispartof>International journal of advanced manufacturing technology, 2015-05, Vol.78 (5-8), p.1031-1041</ispartof><rights>Springer-Verlag London 2014</rights><rights>The International Journal of Advanced Manufacturing Technology is a copyright of Springer, (2014). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c386t-c6e7123818b1ecd46bc5135bf30f0b19a8dda2f4f6779ee8858e6582273111953</citedby><cites>FETCH-LOGICAL-c386t-c6e7123818b1ecd46bc5135bf30f0b19a8dda2f4f6779ee8858e6582273111953</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00170-014-6707-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00170-014-6707-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Azam, Muhammad</creatorcontrib><creatorcontrib>Jahanzaib, Mirza</creatorcontrib><creatorcontrib>Wasim, Ahmad</creatorcontrib><creatorcontrib>Hussain, Salman</creatorcontrib><title>Surface roughness modeling using RSM for HSLA steel by coated carbide tools</title><title>International journal of advanced manufacturing technology</title><addtitle>Int J Adv Manuf Technol</addtitle><description>Surface roughness is the main indicator of surface quality on machined parts. Accurate predictive models for surface roughness help to choose optimum machining parameters, ultimately support to maximize the productivity without any compromise on quality. In this paper, an average surface roughness (
R
a
) model has been developed for turning high-strength low-alloy steel (AISI 4340 with carbon contents less than 0.3 %) using multilayer coated carbide tools. A series of tests using response surface methodology (RSM) has been employed to develop a relationship between
R
a
and machining parameters (feed, speed, and depth of cut). The feed rate has been observed as the main parameter that influences surface roughness. Contour plots of “feed versus speed” and “feed versus depth of cut” signify that target
R
a
value can be achieved through optimal combination of cutting parameters. The accuracy of proposed model has been confirmed through validation data with average prediction error of 3.38 %.</description><subject>CAE) and Design</subject><subject>Carbide tools</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Cutting parameters</subject><subject>Engineering</subject><subject>Feed rate</subject><subject>High strength low alloy steels</subject><subject>Industrial and Production Engineering</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Model accuracy</subject><subject>Multilayers</subject><subject>Optimization</subject><subject>Original Article</subject><subject>Prediction models</subject><subject>Production planning</subject><subject>Response surface methodology</subject><subject>Surface properties</subject><subject>Surface roughness</subject><subject>Test procedures</subject><subject>Tool steels</subject><subject>Turning (machining)</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1kMFKAzEQhoMoWKsP4C3gOZpJdpP0WIpasSJYPYfd7KS2bDc12T307bvLCp6EYeby_f_AR8gt8HvgXD8kzkFzxiFjSnPN8jMygUxKJjnk52TChTJMamUuyVVKu55WoMyEvK676AuHNIZu891gSnQfKqy3zYZ2adgf6zfqQ6TL9WpOU4tY0_JIXSharKgrYrmtkLYh1OmaXPiiTnjze6fk6-nxc7Fkq_fnl8V8xZw0qmVOoQYhDZgS0FWZKl0OMi-95J6XMCtMVRXCZ15pPUM0JjeociOElgAwy-WU3I29hxh-Okyt3YUuNv1LK4TqhyshewpGysWQUkRvD3G7L-LRAreDMzs6s70zOzizQ7MYM6lnmw3Gv-b_QycF_mz6</recordid><startdate>20150501</startdate><enddate>20150501</enddate><creator>Azam, Muhammad</creator><creator>Jahanzaib, Mirza</creator><creator>Wasim, Ahmad</creator><creator>Hussain, Salman</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></search><sort><creationdate>20150501</creationdate><title>Surface roughness modeling using RSM for HSLA steel by coated carbide tools</title><author>Azam, Muhammad ; Jahanzaib, Mirza ; Wasim, Ahmad ; Hussain, Salman</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c386t-c6e7123818b1ecd46bc5135bf30f0b19a8dda2f4f6779ee8858e6582273111953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>CAE) and Design</topic><topic>Carbide tools</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Cutting parameters</topic><topic>Engineering</topic><topic>Feed rate</topic><topic>High strength low alloy steels</topic><topic>Industrial and Production Engineering</topic><topic>Mechanical Engineering</topic><topic>Media Management</topic><topic>Model accuracy</topic><topic>Multilayers</topic><topic>Optimization</topic><topic>Original Article</topic><topic>Prediction models</topic><topic>Production planning</topic><topic>Response surface methodology</topic><topic>Surface properties</topic><topic>Surface roughness</topic><topic>Test procedures</topic><topic>Tool steels</topic><topic>Turning (machining)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Azam, Muhammad</creatorcontrib><creatorcontrib>Jahanzaib, Mirza</creatorcontrib><creatorcontrib>Wasim, Ahmad</creatorcontrib><creatorcontrib>Hussain, Salman</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>Azam, Muhammad</au><au>Jahanzaib, Mirza</au><au>Wasim, Ahmad</au><au>Hussain, Salman</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Surface roughness modeling using RSM for HSLA steel by coated carbide tools</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2015-05-01</date><risdate>2015</risdate><volume>78</volume><issue>5-8</issue><spage>1031</spage><epage>1041</epage><pages>1031-1041</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>Surface roughness is the main indicator of surface quality on machined parts. Accurate predictive models for surface roughness help to choose optimum machining parameters, ultimately support to maximize the productivity without any compromise on quality. In this paper, an average surface roughness (
R
a
) model has been developed for turning high-strength low-alloy steel (AISI 4340 with carbon contents less than 0.3 %) using multilayer coated carbide tools. A series of tests using response surface methodology (RSM) has been employed to develop a relationship between
R
a
and machining parameters (feed, speed, and depth of cut). The feed rate has been observed as the main parameter that influences surface roughness. Contour plots of “feed versus speed” and “feed versus depth of cut” signify that target
R
a
value can be achieved through optimal combination of cutting parameters. The accuracy of proposed model has been confirmed through validation data with average prediction error of 3.38 %.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00170-014-6707-5</doi><tpages>11</tpages></addata></record> |
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| ispartof | International journal of advanced manufacturing technology, 2015-05, Vol.78 (5-8), p.1031-1041 |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | CAE) and Design Carbide tools Computer-Aided Engineering (CAD Cutting parameters Engineering Feed rate High strength low alloy steels Industrial and Production Engineering Mechanical Engineering Media Management Model accuracy Multilayers Optimization Original Article Prediction models Production planning Response surface methodology Surface properties Surface roughness Test procedures Tool steels Turning (machining) |
| title | Surface roughness modeling using RSM for HSLA steel by coated carbide tools |
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