Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling |
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| Authors: | HS Lu CK Chang NC Hwang CT Chung |
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| Affiliation: | 1. Department of Mechanical and Computer-Aided Engineering, National Formosa University, 64, Wun-Hun Road, Huwei, Yunlin 632, Taiwan;2. Institute Engineering Science and Technology, National Kaohsiung First University of Science and Technology, 2, Juoyue Road, Nantz District, Kaohsiung 811, Taiwan;3. Department of Vehicle Engineering, National Formosa University, 64, Wun-hun Road, Huwei, Yunlin 632, Taiwan;1. Department of Mechanical Engineering, Sinop University, 57030 Sinop, Turkey;2. Manufacturing Department, Technology Faculty, Gazi University, 06500 Ankara, Turkey;1. Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy;2. Laboratory of Machine Tools and Production Systems, Consorzio MUSP, Piacenza, Italy;3. Department of Industrial Engineering and Management, Jiao Tong University, Shanghai, China;1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, PR China;2. College of Engineering and Technology, Southwest University, Chongqing, PR China;3. Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA;4. College of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, PR China;5. School of Information Science and Technology at Maritime University, Dalian, PR China |
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| Abstract: | This paper investigates optimization design of the cutting parameters for rough cutting processes in high-speed end milling on SKD61 tool steel. The major characteristics indexes for performance selected to evaluate the processes are tool life and metal removal rate, and the corresponding cutting parameters are milling type, spindle speed, feed per tooth, radial depth of cut, and axial depth of cut. In this study, the process is intrinsically with multiple performance indexes so that grey relational analysis that uses grey relational grade as performance index is specially adopted to determine the optimal combination of cutting parameters. Moreover, the principal component analysis is applied to evaluate the weighting values corresponding to various performance characteristics so that their relative importance can be properly and objectively described. The results of confirmation experiments reveal that grey relational analysis coupled with principal component analysis can effectively acquire the optimal combination of cutting parameters. Hence, this confirms that the proposed approach in this study can be an useful tool to improve the cutting performance of rough cutting processes in high-speed end milling process. |
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