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Journal of Engineering and Applied Sciences
Year: 2017  |  Volume: 12  |  Issue: 6 SI  |  Page No.: 7951 - 7958

Prediction of MAF Parameters for AISI 316 SS using RBFNN and ANN Based Box-Behnken Design

Saad Hameed Al-Shafaie    

Abstract: This study represents work suggestion an ideological technique to solve optimization problem with multi-response enclosing Magnetic Abrasive Finishing (MAF) of stainless steel 316 (AISI 316 SS) using Artificial Neural Network (ANN) and Radial Basics Function Neural Network (RBFNN) methods based on Box Behnken design. The prediction of MAF is done by choosing input process parameters such as number of cycles of pole geometry, cutting velocity, amplitude of pole geometry, current, working gap and finishing time, whereas the output responses were Metal Removal Rate (MRR) and Surface Roughness (SR). Each node achieves an easy process in calculating its response from its independent variable that is conveyed through links joined to another. It is concluded that the error obtained in RBFNN Model is bigger than that ANN Model. In the end, it was proved that the create network’s model was built using ANN tool that gives the predicted result when compared to the RBFNN Model.

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