Nowadays, machining of hard-to-machine alloys has become a challenge in terms of coping with di erent approaches that have been introduced so far, among which Abrasive Water Jet Machining (AWJM) has become one of the most extensively used ones owing to its advantages. The current study provided the required data for modeling, statistical analysis, and optimization of AWJM process based on Taguchi Orthogonal Array (OA) and D-optimal approaches. Regression modeling was also considered to relate the process input variables (water pressure, abrasive ow rate, machining speed, and machining gap) to the output characteristic namely Surface Roughness (SR). In this regard, three sets of models were proposed using three experimental matrices namely OA-Taguchi, D- optimal, and their combination, and their adequacy was checked using Analysis of Variance (ANOVA). According to the ndings, the most signi cant variable a ecting SR was the machining speed with the contribution of 66%. Finally, to optimize the objective functions of the proposed models and obtain the optimized (the least) characteristic (SR), the models were embedded in Simulated Annealing (SA) algorithm. According to the computational results, the mixture matrix (with less than 4% error) was superior to OA-Taguchi and D-optimal, hence quite being ecient in modeling and optimizing the process.

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