Artificial neural network–based and response surface methodology–based predictive models for material removal rate and surface roughness during electro-discharge diamond grinding of Inconel 718

Hybrid machining processes growing popularity in the processing of difficult-to-cut materials due to their distinct merits over individual machining processes attributed by an amalgamation of two or more machining mechanisms simultaneously. This research study deals with the response surface methodology and artificial neural network with backpropagation algorithm–based mathematical modeling of electro-discharge diamond grinding of Inconel 718 superalloy. The matrix experiments were designed based on central composite design. The wheel speed, current, pulse-on-time, and duty factor were chosen as control factors, while material removal rate and average surface roughness ( R a ) were chosen as performance parameters. The analysis of variance test shows that the wheel speed is the major factor influencing both the material removal rate and the R a and contributes 89.03% and 79.10% on material removal rate and R a , respectively, followed by current which contributes 4.43% and 8.38% on material removal rate and R a , respectively. The modeling and predictive abilities of developed artificial neural network model (4-24-2) were related to the response surface methodology model using root mean square error and absolute standard deviation. The predicted values of material removal rate and R a by response surface methodology and artificial neural network are in close agreement with the actual experimental results.