Using Gray Relational Analysis and Taguchi Technique in Solving Multi-objective Problems for Turning Operation of Austenitic Stainless Steel

Journal of Mechanical Research and Application

مقاله 7، دوره 4، شماره 3، اسفند 2012، صفحه 57-69   

نویسندگان

M Azadi Moghaddam¹؛ F Kolahan²؛ M Hassani Doughabadi³

¹M. Sc. Phd-student, Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

²Associate Professor, Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

³Young Researchers Club, Student Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

چکیده

In this study, the application of gray relational analysis (GRA) and Taguchi method in multi-criteria process parameters selection of turning operation has been investigated. The process responses under study are material removal rate (MRR) and surface roughness (SR); in turn, the input parameters include cutting speed, feed rate, depth of cut and nose radius of the cutting tool. The proposed approach employs GRA to convert the values of process outputs, obtained from Taguchi method, into a single objective used to determine the best set of process parameters for turning operation of AISI 202 austenitic stainless steel. Analytical results reveal that the combination of higher levels of cutting speed, depth of cut, and nose radius and lower level of feed rate is essential to achieve simultaneous maximization of material removal rate and minimization of surface roughness. Using verification test, these settings would result in more than 14% improvement over those for initial settings. The analysis of variance (ANOVA) and F-tests showed that nose radius is the major factor affecting the gray relational grade (GRG) with 41% contribution. In general, the proposed procedure is quite efficient in determining the effects of process parameters and finding the best set of process parameters.

کلیدواژه‌ها

Taguchi Technique؛ Gray Relational Analysis؛ multi objective optimization؛ Austenitic Stainless Steel؛ analysis of variance