Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA

In this study, Artificial Neural Network (ANN) and Simulated Annealing (SA) techniques were integrated labeled as integrated ANN-SA to estimate optimal process parameters in abrasive waterjet (AWJ) machining operation. The considered process parameters include traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate. The quality of the cutting of machined-material is assessed by looking to the roughness average value (Ra). The optimal values of the process parameters are targeted for giving a minimum value of Ra. It was evidence that integrated ANN-SA is capable of giving much lower value of Ra at the recommended optimal process parameters compared to the result of experimental and ANN single-based modeling. The number of iterations for the optimal solutions is also decreased compared to the result of SA single-based optimization.

Research highlights
► Integrated ANN-SA was proposed to estimate optimal machining process parameters.
► The optimal process parameters were expected to give a minimum surface roughness, Ra.
► Result showed that the integrated ANN-SA reduced minimum Ra of ANN model at 44.5%.
► The proposed approach has also reduced minimum Ra of SA optimization at 0.81%.