Multi-objective parametric optimization of AWJM process using Taguchi-based GRA and DEAR methodology

Abstract

Abrasive waterjet machining (AWJM) is a well-known non-traditional cutting process that is widely used to shape materials such as metals, ceramics, alloys, and composites. AWJM was utilized to manufacture titanium alloy in this study, with influencing parameters such as standoff distance (SOD), abrasive flow rate (AFR), and traverse speed (TS) being varied. Material removal rate (MRR) and surface roughness (SR) have been used to quantify the impact of these variables on machining quality. The effects of process factors on output responses were investigated using a Taguchi study approach with an L9 factorial design. SOD of 1 mm, AFR of 300 g/min, and TS of 519 m/s yield the best combination for greater MRR. A combination of SOD of 3 mm, AFR of 400 g/min, and TS of 519 m/s yields the best SR results. Simultaneous optimization of both outputs was also carried out using Taguchi-based Grey relational analysis (GRA) and data envelopment analysis-based ranking (DEAR) methodologies. PCA is utilized for considering the weights of responses and the correlation among them. Both methods obtained similar results, but there is a difference in the middle rankings in the nine experimental trials. Validation trials were run on the acquired combination of parameters, and the error was found to be within acceptable bounds.