Abstract
This paper attempts to model the electrical discharge face grinding (EDFG) process, while machining the Monel 400 superalloy, using the response surface methodology (RSM) and artificial neural network (ANN) for process performance prediction. Initially, the experiments were designed using the central composite design of RSM. The grinding wheel speed, peak current, pulse-on-time, and pulse-off-time were chosen as input process parameters, while tool wear rate (TWR) as the performance attributes. Analysis of variance was to identify the significant model terms and their influence on TWR. The grinding wheel speed was identified as the most influencing parameter, and a 61% reduction in TWR was recorded when increasing the speed from 200 rpm to 600 rpm. The developed ANN model (4-20-1) reduced the mean square prediction error up to four times, outperforming the RSM model.
Published Version
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