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Abrasive Water Jet Machining (AWJM) is a non-traditional machining process renowned for its versatility and ability to cut a wide range of materials precisely. This research article presents an in-depth analysis of the optimization of AWJM parameters for machining 316 stainless steel, aiming to enhance surface quality and machining efficiency. Through a comprehensive experimental setup, the study explores the effects of varying the speed, standoff distance (SOD), and flow rate on the surface roughness (Ra) of the machined workpiece. The Taguchi method's L9 orthogonal array is employed to design the experiments, and a signal-to-noise (S/N) ratio analysis, alongside an analysis of variance (ANOVA), is utilized to discern the most significant machining parameters. Response tables for S/N ratios and means are created to summarize the effects, and main effects plots are generated to visualize trends in the data. Furthermore, a regression model is developed to correlate the machining parameters with the surface roughness, which is validated by a high coefficient of determination. Residual plots and diagnostics for unusual observations are utilized to ensure the robustness of the model. The study concludes that SOD is the most influential parameter, followed by speed and flow rate. The optimization results provide a quantitative understanding that can significantly contribute to the industrial application of AWJM for 316 stainless steel, ensuring optimal surface integrity and operational cost-effectiveness. The findings of this research offer pivotal insights for manufacturing industries that seek to integrate AWJM into their production processes.
Bhat et al. (Wed,) studied this question.
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