Optimization in CNC end milling of UNS C34000 medimum leaded brass with multiple surface roughnesses characteristics

The present study highlights a multi-objective optimization problem by applying utility concept coupled with Taguchi method through a case study in CNC end milling of UNS C34000 medium leaded brass. The study aimed at evaluating the best process environment which could simultaneously satisfy multiple requirements of surface quality. In view of the fact, the traditional Taguchi method cannot solve a multi-objective optimization problem; to overcome this limitation, utility theory has been coupled with Taguchi method. Depending on Taguchi’s Lower-the-Better (LB) response criteria; individual surface quality characteristics has been transformed into corresponding utility values. Individual utility values have been aggregated finally to compute overall utility degree which serves as representative objective function for optimizing using Taguchi method. Utility theory has been adopted to convert a multi-response optimization problem into a single response optimization problem; in which overall utility degree serves as the representative single objective function for optimization. The study of combined utility theory and Taguchi method for predicting optimal setting. Based on Taguchi’s Signal-to-Noise ratio (S/N), analysis has been made on the overall utility degree and optimal process environment has been selected finally which corresponds to highest S/N Ratio. Optimal result has been verified through confirmatory test. The case study indicates application feasibility of the aforesaid methodology proposed for multiresponse optimization and off-line control of multiple surface quality characteristics in CNC end milling.

[1]  Jiju Antony,et al.  Teaching the Taguchi method to industrial engineers , 2001 .

[2]  Pradeep Kumar,et al.  Quality optimization (multi-characteristics) through Taguchi's technique and utility concept , 2000 .

[3]  R. Sagherian,et al.  Dynamic modeling for the prediction of surface errors in the milling of thin-walled sections , 1991 .

[4]  V. M. Huynh,et al.  Surface-texture measurement and characterisation with applications to machine-tool monitoring , 1992 .

[5]  A. Mansour,et al.  Surface roughness model for end milling: a semi-free cutting carbon casehardening steel (EN32) in dry condition , 2002 .

[6]  S. G. Deshmukh,et al.  A genetic algorithmic approach for optimization of surface roughness prediction model , 2002 .

[7]  Mirko Ficko,et al.  Prediction of surface roughness with genetic programming , 2004 .

[8]  R. S. Walia,et al.  Multi-Response Optimization of CFAAFM Process Through Taguchi Method and Utility Concept , 2006 .

[9]  Steven R Schmid Kalpakjian,et al.  Manufacturing Engineering and Technology , 1991 .

[10]  George-Christopher Vosniakos,et al.  Predicting surface roughness in machining: a review , 2003 .

[11]  John L. Yang.  A Systematic Approach for Identifying Optimum Surface Roughness Performance in End-Milling Operations By Mr . , 2001 .

[12]  Rishi Singal,et al.  Fundamentals of Machining and Machine Tools , 2008 .

[13]  Joseph C. Chen,et al.  An in-process surface recognition system based on neural networks in end milling cutting operations , 1999 .

[14]  S. J. Lou,et al.  In-process surface recognition of a CNC milling machine using the fuzzy nets method , 1997 .

[15]  M. Alauddin,et al.  Computer-aided analysis of a surface-roughness model for end milling , 1995 .

[16]  John S. Agapiou,et al.  Metal Cutting Theory and Practice , 1996 .

[17]  Oğuz Çolak,et al.  Milling surface roughness prediction using evolutionary programming methods , 2007 .

[18]  Ching-Kao Chang,et al.  Design optimization of cutting parameters for side milling operations with multiple performance characteristics , 2007 .

[19]  George-Christopher Vosniakos,et al.  Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments , 2002 .

[20]  Shi Hyoung Ryu,et al.  Roughness and texture generation on end milled surfaces , 2006 .

[21]  G. Boothroyd,et al.  Fundamentals of machining and machine tools , 2006 .

[22]  Babur Ozcelik,et al.  The statistical modeling of surface roughness in high-speed flat end milling , 2006 .

[23]  Seung-Han Yang,et al.  A study on roughness of the micro-end-milled surface produced by a miniatured machine tool , 2005 .

[24]  Hasan Kurtaran,et al.  Application of response surface methodology in the optimization of cutting conditions for surface roughness , 2005 .

[25]  Shi-Jer Lou,et al.  In-Process Surface Roughness Recognition (ISRR) System in End-Milling Operations , 1999 .

[26]  W. Kline,et al.  The Prediction of Surface Accuracy in End Milling , 1982 .

[27]  John Stufken,et al.  Taguchi Methods: A Hands-On Approach , 1992 .

[28]  Ming-Yung Wang,et al.  Experimental study of surface roughness in slot end milling AL2014-T6 , 2004 .

[29]  P. N. Rao,et al.  Manufacturing technology : metal cutting & machine tools , 2000 .

[30]  Imtiaz Ahmed Choudhury,et al.  Application of Taguchi method in the optimization of end milling parameters , 2004 .