Taguchi approach followed by fuzzy linguistic reasoning for quality‐productivity optimization in machining operation

Abstract

PurposeThe study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.Design/methodology/approachIn this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L9 orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting FIS. A multi‐performance characteristic index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method.FindingsThe study demonstrates application feasibility of the proposed approach with satisfactory result of confirmatory test. The proposed procedure is simple, and effective in developing a robust, versatile and flexible mass production process.Originality/valueIn the proposed model it is not required to assign individual response weights; no need to check for response correlation. FIS can efficiently take care of these aspects into its internal hierarchy thereby overcoming various limitations/assumptions of existing optimization approaches.