Optimization of MRR, Surface Roughness, and Maximum Tool-Tip Temperature during Machining of CFRP Composites

Recently, machining of carbon fiber reinforced polymer (CFRP) composites has become a vital concern for the modern manufacturing companies due to the widespread applications of CFRP machined parts especially in automobile as well as aerospace industries. Owing to the necessity of mass-production of CFRP component parts, it is indeed essential to optimize the machining process parameters in order to improve process performance yields in terms of product quality economically. Overall performance of the machining (turning) process is influenced by different process parameters such as spindle speed, feed rate and depth of cut. In machining of CFRP composites, Material Removal Rate (MRR), surface roughness and tool-tip temperature are generally considered as the output responses. In the present work, the extent of process performance has been evaluated in turning of CFRP composites using PVD coated carbide tool. For optimization of multiple responses, a Fuzzy Inference System (FIS) has been used to convert multiple responses into an equivalent single response known called as Multi-Performance Characteristic Index (MPCI). A non-linear regression model has been developed in expressing MPCI as a function of the selected process parameters. The said regression model has been considered as the fitness function and finally optimized by two evolutionary techniques known as Harmony Search (HS) algorithm and Teaching-Learning-Based Optimization (TLBO) algorithm. The effectiveness of the aforesaid algorithm has been compared to that of Taguchi's robust optimization philosophy. (C) 2017 Elsevier Ltd. All rights reserved.