Parametric Optimization of AWJM Using RSM-Grey-TLBO-Based MCDM Approach for Titanium Grade 5 Alloy

Abstract

Abrasive water jet machining (AWJM) is an incredibly effective method for processing challenging materials, overcoming the obstacles encountered when working with them. High-pressure water combined with abrasive particles is used to erode and penetrate the workpiece material. Processing titanium grade 5 alloy can be a complex task, but it is possible to efficiently machine it using abrasive water jet machining. The study analyzes the impact of pressure (P), abrasive flow rate (AFRE), stand-off distance (SoD) and traverse speed (TRS). A Taguchi L25 array (orthogonal) was utilized for carrying out the experiments. The best process parameters were identified through response surface methodology in order to reduce processing time (PT) and surface roughness (SR), while increasing hardness (HRC). The results, including processing time, surface roughness, and hardness, were transformed into a composite grade through the application of grey relational analysis. The empirical model was formulated utilizing the teaching–learning-based optimization (TLBO) technique and the best process parameters were investigated using RSM-Grey-TLBO-based multi-criteria decision-making. The RSM-Grey-TLBO MCDM method proposes an optimized configuration for GRG (mean method) with parameters P = 320 MPa, SoD = 4 mm, TRS = 190 m/min, AFRE = 12 g/sec and for the weighted method of GRG with parameters P = 320 MPa, SoD = 8 mm, TRS-150 m/min, AFRE-9 g/sec. The percentage inaccuracies for the forecasted errors are 7.47% and 7.33% in GRG (mean method) and GRG (weighted method), respectively.