Multi-response optimization in machining: exploration of TOPSIS and Deng’s similarity based approach

Abstract

Machining deals with removal of unwanted material from the work piece in the form of chips in order to get required dimension. Consumption of energy, wastage of material, requirement of skilled person etc. make the process expensive. Hence, machining industries have to face the inevitable challenge to reduce cost as well as to machine material within the tolerance limit which can be accepted by the customers. The output characteristics like Material Removal Rate (MRR), surface roughness, tool wear, tool life, cutting temperature, cutting force etc. are greatly influenced by the input cutting parameters like speed, feed rate, depth of cut etc. Therefore, selection of cutting parameter plays an important role for a sound production. Optimization techniques are quite helpful for selection of appropriate cutting parameters through offline check. The industries have to concern about a number of performance characteristics simultaneously because focus on a single objective may appear as loss for rest of the objectives, and, hence, multi-objective optimization techniques may be suitable. In the present work, turning operation of aluminum was carried out using a HSS tool on a lathe machine. Cutting parameters: speed, feed rate, and depth of cut was varied at five different levels; Taguchi method was employed for designing a L25 orthogonal array. The output performances viz. MRR, surface roughness, cutting temperature, and cutting forces were recorded for each run. Deng’s similarity based method and TOPSIS (integrated with Taguchi method) were explored for determining appropriate process environment (parameter setting) for simultaneous optimization of multiple process-performance-yields.