The goal of this study is to use a multi-response Taguchi-grey relational technique with Artificial Neural Network (ANN) to optimise the machining parameters of Ti-6Al-4V nano-alloy in the abrasive water jet (AJM) process. The emphasis is on discovering the ideal input factors that have a substantial impact on the workpiece's material removal rate (MRR) and surface quality. The study focuses on the effect of abrasive content characteristics such as SiC volume, SiC size, and abrasive flow rate. Because of its flexibility to handle numerous input variables and produce various sets of optimum values for each response, the Taguchi-grey relational with ANN technique was chosen. The results show that adding SiC in conjunction with garnet enhances the MRR—the best combination of 1wt. The study identifies a SiC addition, an abrasive flow rate of 6g/s, and a 100-mesh SiC size. This results in an MRR of 0.5386 MPa and a surface roughness of 3.32m. Images taken with a scanning electron microscope (SEM) show the machined surface of the titanium nano-alloy from several machining attempts. Overall, the Taguchi-grey relational technique is successful for optimising the machining process parameters for Ti-6Al-4V nano-alloy in AJM, resulting in better MRR and surface quality.
Sivaraman et al. (Tue,) studied this question.