Application of Response Surface Methodology and Firefly Algorithm for Optimizing Multiple Responses in Turning AISI 1045 Steel

Optimization of machining parameters considering multiple responses flank wear, surface roughness, and material removal rate (MRR) simultaneously are performed using response surface methodology (RSM). The workpiece material chosen for turning is AISI 1045, medium carbon steel, and uncoated carbide tool inserts. Twenty experiments are designed based on face-centered center composite design for three numerical parameters such as cutting speed, feed rate, and depth of cut. In this work, wear at the flank face of the cutting tool insert and surface roughness at the machined surface are to be minimized, whereas the MRR has to be maximized. With the obtained optimum condition, a confirmation experiment is performed and the experimental results obtained are flank wear of 0.118 mm, surface roughness of 3.27 mu m, and MRR of 187.35 gm/min, which shows that prediction using RSM is within the acceptable range. Along with the combined optimization of these responses, a quadratic empirical model is generated for each response. An evolutionary optimization algorithm, firefly algorithm, is applied to determine the optimum machining parameters for the chosen objective of lowering flank wear and increasing MRR within a specific surface roughness value.