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

Authors:
Azlan Mohd Zain;Habibollah Haron;Safian Sharif
Affiliations:
Faculty of Computer Science & Information System, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia;Faculty of Computer Science & Information System, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia;Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

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 (R"a). The optimal values of the process parameters are targeted for giving a minimum value of R"a. It was evidence that integrated ANN-SA is capable of giving much lower value of R"a 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.