MULTI-RESPONSE OPTIMIZATION OF BALL-END MILLING PARAMETERS USING THE TAGUCHI-BASED GREY RELATIONAL ANALYSIS

This paper presents an approach for optimization of machining parameters with multi-response outputs using design of experiment in ball-end milling. During the ball-end milling of hardened steel, process performance indicators such as surface roughness, material removal rate and resultant cutting force were measured. The process parameters which are spindle speed, feed per tooth, axial depth of cut and radial depth of cut were simultaneously optimized by the Taguchi-based Grey relational analysis. Experiments are designed and conducted based on Taguchi's L25 orthogonal array design. Based on grey relational grade value, optimum levels of parameters have been identified by using response table and response graph and the significant contributions of controlling parameters are estimated using analysis of variances (ANOVA). Confirmation test is conducted for the optimal machining parameters to validate the test result.