Optimization of Turning Process Parameters of Al7075/FA/SiC Hybrid Composite using Evolutionary Algorithms

Abstract

This paper explores the importance of two nature-inspired optimization algorithms, Particle Swarm Optimization (PSO) and Invasive Weed Optimization (IWO) for identifying the optimal turning process parameters of Al7075/FA/SiC hybrid composite. Initially, experimentation is carried out with the help of Taguchi’s L16 OA. Further, the non-linear regression equations are established for surface roughness (Ra) and material removal rate (MRR). The three independent parameters namely, cutting speed (m/min), feed rate (mm/rev), and depth of cut (mm) are considered to optimize Ra and MRR. Initially, Ra and MRR are optimized independently. Later on, the weighted sum process is used, which is helpful to convert the dissimilar responses into a single response, and then multi-objective optimization is executed using PSO and IWO. Finally, the optimum results obtained by these methods are compared to identify the best method. It has been observed that PSO algorithm performed better compared to IWO algorithm in relationships of minimization of the bi-objective function value.