In this present work, the parametric optimization of ELID grinding process of Al/SiC composite through a neuro-fuzzy network is studied. Electrolytic In- Process Dressing (ELID) grinding can be used to machine hard and brittle materials to achieve high surface quality and high material removal rate. The Design of Experiments (DOE) technique is developed for five factors at three levels. Experiments have been conducted for measuring surface roughness, hardness and metal removal rate based on the DOE technique in an ELID grinding machine using a diamond wheel. The experimentally measured values are also used to train the feed forward back propagation neuro-fuzzy for prediction of surface roughness. The predictive neuro fuzzy model was found to be capable of better prediction of surface roughness, hardness and metal removal rate within the trained range.
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