Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process
We observe a surface roughness in end milling machining process which is influenced by machine parameters, namely radial rake angle, speed and feed rate cutting condition. In this machining, we need to minimize and to obtain as low as possible the surface roughness by determining the optimum values...
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| Format: | Article |
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Elsevier Ltd.
2012
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| _version_ | 1796859093869133824 |
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| author | Wibowo, Antoni Desa, Mohammad Ishak |
| author_facet | Wibowo, Antoni Desa, Mohammad Ishak |
| author_sort | Wibowo, Antoni |
| collection | ePrints |
| description | We observe a surface roughness in end milling machining process which is influenced by machine parameters, namely radial rake angle, speed and feed rate cutting condition. In this machining, we need to minimize and to obtain as low as possible the surface roughness by determining the optimum values of the three parameters. In previous works, some researchers used a response surface methodology (RSM) and a soft-computing approach, which was based on ordinary linear regression and genetic algorithms (GAs), to estimate the minimum surface roughness and its corresponding values of the parameters. However, the construction of the ordinary regression models was conducted without considering the existence of multicollinearity which can lead to inappropriate prediction. Beside that it is known the relation between the surface roughness and the three parameters is nonlinear, which implies that a linear regression model can be inappropriate model to approximate it. In this paper, we present a technique developed using hybridization of kernel principal component analysis (KPCA) based nonlinear regression and GAs to estimate the optimum values of the three parameters such that the estimated surface roughness is as low as possible. We use KPCA based regression to construct a nonlinear regression and to avoid the effect of multicollinearity in its prediction model. We show that the proposed technique gives more accurate prediction model than the ordinary linear regression’s approach. Comparing with the experiment data and RSM, our technique reduces the minimum surface roughness by about 45.3% and 54.2%, respectively. |
| first_indexed | 2024-03-05T19:22:05Z |
| format | Article |
| id | utm.eprints-47144 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:22:05Z |
| publishDate | 2012 |
| publisher | Elsevier Ltd. |
| record_format | dspace |
| spelling | utm.eprints-471442019-03-31T08:34:27Z http://eprints.utm.my/47144/ Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process Wibowo, Antoni Desa, Mohammad Ishak QA76 Computer software We observe a surface roughness in end milling machining process which is influenced by machine parameters, namely radial rake angle, speed and feed rate cutting condition. In this machining, we need to minimize and to obtain as low as possible the surface roughness by determining the optimum values of the three parameters. In previous works, some researchers used a response surface methodology (RSM) and a soft-computing approach, which was based on ordinary linear regression and genetic algorithms (GAs), to estimate the minimum surface roughness and its corresponding values of the parameters. However, the construction of the ordinary regression models was conducted without considering the existence of multicollinearity which can lead to inappropriate prediction. Beside that it is known the relation between the surface roughness and the three parameters is nonlinear, which implies that a linear regression model can be inappropriate model to approximate it. In this paper, we present a technique developed using hybridization of kernel principal component analysis (KPCA) based nonlinear regression and GAs to estimate the optimum values of the three parameters such that the estimated surface roughness is as low as possible. We use KPCA based regression to construct a nonlinear regression and to avoid the effect of multicollinearity in its prediction model. We show that the proposed technique gives more accurate prediction model than the ordinary linear regression’s approach. Comparing with the experiment data and RSM, our technique reduces the minimum surface roughness by about 45.3% and 54.2%, respectively. Elsevier Ltd. 2012 Article PeerReviewed Wibowo, Antoni and Desa, Mohammad Ishak (2012) Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process. Expert Systems with Applications, 39 (14). pp. 11634-11641. ISSN 0957-4174 http://dx.doi.org/10.1016/j.eswa.2012.04.004 DOI:10.1016/j.eswa.2012.04.004 |
| spellingShingle | QA76 Computer software Wibowo, Antoni Desa, Mohammad Ishak Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process |
| title | Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process |
| title_full | Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process |
| title_fullStr | Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process |
| title_full_unstemmed | Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process |
| title_short | Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process |
| title_sort | kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process |
| topic | QA76 Computer software |
| work_keys_str_mv | AT wibowoantoni kernelbasedregressionandgeneticalgorithmsforestimatingcuttingconditionsofsurfaceroughnessinendmillingmachiningprocess AT desamohammadishak kernelbasedregressionandgeneticalgorithmsforestimatingcuttingconditionsofsurfaceroughnessinendmillingmachiningprocess |