Workpiece surface temperature for in-process surface roughness prediction using response surface methodology
As manufacturing technology has been moving to the stage of full automation over the years, one of the fundamental requirements is the ability to accurately predict the output performance of machining processes. The focus of present study is to predict surface roughness using the workpiece surface t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Asian Network for Scientific Information
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/23030/ http://psasir.upm.edu.my/id/eprint/23030/1/Workpiece%20surface%20temperature%20for%20in-process%20surface%20roughness%20prediction%20using%20response%20surface%20methodology.pdf |
| _version_ | 1848844645341069312 |
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| author | Suhail, Adeel H. Ismail, Napsiah Wong, Shaw Voon Abdul Jalil, Nawal Aswan |
| author_facet | Suhail, Adeel H. Ismail, Napsiah Wong, Shaw Voon Abdul Jalil, Nawal Aswan |
| author_sort | Suhail, Adeel H. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | As manufacturing technology has been moving to the stage of full automation over the years, one of the fundamental requirements is the ability to accurately predict the output performance of machining processes. The focus of present study is to predict surface roughness using the workpiece surface temperature of a turning workpiece with the aid of an infrared temperature sensor. Relationship between the workpiece surface temperature and the cutting parameters and also between the surface roughness and cutting parameters were found out for indirect measurement of surface roughness through the surface temperature of the workpiece. A 33 full factorial design was used in order to get the output data uniformly distributed all over the ranges of the input parameters. Response Surface Method (RSM) and analysis of variance (ANOVA) are used to get the relation between different response variables (Surface roughness and workpiece surface temperature) and the input parameters (speed, feed and depth of cut). Based on variance analysis for the second order RSM model, most influential design variable is feed rate and depth of cut on surface roughness and workpiece surface temperature respectively and the experimental results show that the workpiece surface temperature can be sensed and used effectively as an indicator of the cutting performance. |
| first_indexed | 2025-11-15T08:34:13Z |
| format | Article |
| id | upm-23030 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:34:13Z |
| publishDate | 2011 |
| publisher | Asian Network for Scientific Information |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-230302015-11-30T06:03:03Z http://psasir.upm.edu.my/id/eprint/23030/ Workpiece surface temperature for in-process surface roughness prediction using response surface methodology Suhail, Adeel H. Ismail, Napsiah Wong, Shaw Voon Abdul Jalil, Nawal Aswan As manufacturing technology has been moving to the stage of full automation over the years, one of the fundamental requirements is the ability to accurately predict the output performance of machining processes. The focus of present study is to predict surface roughness using the workpiece surface temperature of a turning workpiece with the aid of an infrared temperature sensor. Relationship between the workpiece surface temperature and the cutting parameters and also between the surface roughness and cutting parameters were found out for indirect measurement of surface roughness through the surface temperature of the workpiece. A 33 full factorial design was used in order to get the output data uniformly distributed all over the ranges of the input parameters. Response Surface Method (RSM) and analysis of variance (ANOVA) are used to get the relation between different response variables (Surface roughness and workpiece surface temperature) and the input parameters (speed, feed and depth of cut). Based on variance analysis for the second order RSM model, most influential design variable is feed rate and depth of cut on surface roughness and workpiece surface temperature respectively and the experimental results show that the workpiece surface temperature can be sensed and used effectively as an indicator of the cutting performance. Asian Network for Scientific Information 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23030/1/Workpiece%20surface%20temperature%20for%20in-process%20surface%20roughness%20prediction%20using%20response%20surface%20methodology.pdf Suhail, Adeel H. and Ismail, Napsiah and Wong, Shaw Voon and Abdul Jalil, Nawal Aswan (2011) Workpiece surface temperature for in-process surface roughness prediction using response surface methodology. Journal of Applied Sciences, 11 (2). pp. 308-315. ISSN 1812-5654; ESSN: 1812-5662 http://scialert.net/abstract/?doi=jas.2011.308.315 10.3923/jas.2011.308.315 |
| spellingShingle | Suhail, Adeel H. Ismail, Napsiah Wong, Shaw Voon Abdul Jalil, Nawal Aswan Workpiece surface temperature for in-process surface roughness prediction using response surface methodology |
| title | Workpiece surface temperature for in-process surface roughness prediction using response surface methodology |
| title_full | Workpiece surface temperature for in-process surface roughness prediction using response surface methodology |
| title_fullStr | Workpiece surface temperature for in-process surface roughness prediction using response surface methodology |
| title_full_unstemmed | Workpiece surface temperature for in-process surface roughness prediction using response surface methodology |
| title_short | Workpiece surface temperature for in-process surface roughness prediction using response surface methodology |
| title_sort | workpiece surface temperature for in-process surface roughness prediction using response surface methodology |
| url | http://psasir.upm.edu.my/id/eprint/23030/ http://psasir.upm.edu.my/id/eprint/23030/ http://psasir.upm.edu.my/id/eprint/23030/ http://psasir.upm.edu.my/id/eprint/23030/1/Workpiece%20surface%20temperature%20for%20in-process%20surface%20roughness%20prediction%20using%20response%20surface%20methodology.pdf |