Neural Network Based Modelling and GRA Coupled PCA Optimization of Hole Sinking Electro Discharge Micromachining

Determination of material removal rate (MRR), tool wear rate (TWR) and hole taper (Ta) is a challenging task for manufacturing engineers from the productivity and accuracy point of view of the symmetrical and nonsymmetrical holes due to hole sinking electro discharge micro machining (HS-EDMM) process. Thus, mathematical models for quick prediction of these aspects are needed because experimental determinations of process performances are always tedious and time consuming. Not only prediction but determination of optimum parameter for optimization of process performance is also required. This paper attempts to apply a hybrid mathematical approach comprising of Back Propagation Neural Network (BPNN) for prediction and Grey Relational Analysis (GRA) coupled with Principal Component Analysis (PCA) for optimization with multiple responses of HS-EDMM of Invar-36. Experiments were conducted to generate dataset for training and testing of the network where input parameters consist of gap voltage, capacitance of capacitor and the resulting performance parameters MRR, TWR and Ta. The results indicate that the hybrid approach is capable to predict process output and optimize process performance with reasonable accuracy under varied operating conditions of HS-EDMM. The proposed approach would be extendable to other configurations of EDMM processes for different material. Neural Network Based Modelling and GRA Coupled PCA Optimization of Hole Sinking Electro Discharge Micromachining