Multi-Objective Optimization of Abrasive Waterjet Machining Process Parameters Using Particle Swarm Technique

Abstract

Multi-objective optimization is carried out for the first time to optimize abrasive water jet machining (AWJM) process parameters for graphite. Experiments are carried out by Response Surface Methodology (RSM) using Box-Behnken method. The input process parameters considered are pressure (P), traverse rate (TR) and mesh size (MS). Results are analyzed using Analysis of Variance (ANOVA) and response surface considering individually output parameters such as depth of cut (DOC) and surface roughness (Ra). ANOVA and response surface analyses indicated that similar combinations of AWJM process parameters such as high pressure (176 MPa), medium mesh size (# 100) and low traverse rate (1000 mm/min) resulted in higher depth of cut as well as lower Ra. Therefore, in order to verify the above combinations and to improve productivity, multi-objective optimization is carried out using Particle Swarm Optimization (PSO) to achieve higher depth of cut and low Ra together. From the PSO analysis, it is observed that pressure of 154 MPa, traverse rate of 1877 mm/min and mesh size of # 100 result in high depth of cut and low Ra together. The result obtained from the PSO is compared with that of ANOVA. The outcome of this study will be useful to the manufacturing engineers for selecting appropriate input AWJM process parameters for machining graphite, which has various applications such as aerospace, defence, etc.