Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling

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.

[1]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[2]  Hung-Chang Liao,et al.  Multi-response optimization using weighted principal component , 2006 .

[3]  C. Wang,et al.  Optimizing multiple quality characteristics via Taguchi method-based Grey analysis , 2007 .

[4]  D. Aspinwall,et al.  A review of ultra high speed milling of hardened steels , 1997 .

[5]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[6]  Hung-Cheng Chen,et al.  Optimization of multiple responses using principal component analysis and technique for order preference by similarity to ideal solution , 2005 .

[7]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[8]  J. L Lin,et al.  The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics , 2002 .

[9]  P. S. Kao,et al.  Optimization of electrochemical polishing of stainless steel by grey relational analysis , 2003 .

[10]  C. Fung,et al.  Multi-response optimization in friction properties of PBT composites using Taguchi method and principle component analysis , 2005 .

[11]  Chia-Ming Chang,et al.  The use of grey-based Taguchi methods to determine submerged arc welding process parameters in hardfacing , 2002 .

[12]  Thomas Brett Kirk,et al.  Wear particle classification in a fuzzy grey system , 1999 .

[13]  Jen Fin Lin,et al.  Grey theory applied to evaluate the tribological performances of the a-C:H(N) coating films prepared by differing the nitrogen content and the film thickness , 2006 .

[14]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[15]  T. R. Bement,et al.  Taguchi techniques for quality engineering , 1995 .

[16]  P.Narender Singh,et al.  Optimization by Grey relational analysis of EDM parameters on machining Al–10%SiCP composites , 2004 .

[17]  B. M. Kramer,et al.  On Tool Materials for High Speed Machining , 1987 .