Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract— Hole Drilling Electro Discharge Micro Machining (HD-EDMM), one of the Hybrid Machining Processes (HMPs) combining the features of Electro Discharge Micro Machining (EDMM)) and conventional drilling process is used for machining electrically conducting materials. In the present paper Artificial Neural Network (ANN) model has been proposed for the prediction of material removal rate (MRR), and machined hole overcut (MHO) in Hole Drilling Electro Discharge Micro Machining (HD-EDMM). For this purpose Matlab with the neural networks toolbox (nntool) has been used. The neural network based on process model has also been developed to establish relationship between input process conditions (gap voltage, capacitance, and revolution per minute of tool electrode) and process responses (MRR and MHO). The ANN model has been trained and tested using the data generated from an extensive series of experiments on HDEDMM machine. The trained neural network system has been used to predict MRR and TWR for different input conditions. The ANN model has been found to predict accurately HD-EDMM process responses for chosen process conditions.
Index Terms— Hole Drilling Electro Discharge Micro Machining (HD-EDMM), Artificial Neural Network (ANN), Back-Propagation (BP) algorithm
Cite: Rajesh Kumar Porwal and Vinod Yadava, " ANN Modelling for the Prediction of Material Removal Rate and Machined Hole Overcut in Hole Drilling Electro Discharge Micro Machining," International Journal of Mechanical Engineering and Robotics Research, Vol. 1, No. 2, pp. 174-189, July 2012.
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