Abstract
Hole sinking electrical discharge micromachining (HS-EDMM) is used to create symmetrical micro features of relatively large depth to diameter ratio which is termed as micro hole. HS-EDMM is an efficient technology for micromachining of electrically conductive difficult to machine engineering materials. A predictive artificial neural network (ANN) model for the material removal rate (MRR), tool wear rate and hole taper (Ta) in HS-EDMM process has been proposed in the present paper. For this purpose, MATLAB with the neural network toolbox (nntool) has been used. Training of the model has been performed with data from an extensive series of HS-EDMM experiments on Ti-6Al-4V thin sheet workpiece material. The proposed model uses the gap voltage and capacitance of capacitor as input parameters. The reported results indicate that the proposed ANN model has been found to predict accurately HS-EDMM process response for chosen process conditions.
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