A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263

In this present paper, the machine learning approach is used to optimize, model, and predict factors during drilling Nimonic C263 under dry mode. tough aero alloys, it required find a predictive model optimize in alloy before actual machining process. It helps avoid cost material cost. Experimental trails are planned based on Taguchi analysis, L27 orthogonal array was chosen. Speed, feed, angle of drill were considered as controlling factors, cutting force surface roughness responses. The feed forward neural network (FFNN) develop model. prediction capability validated with developed by analysis. Furthermore, ANOVA (analysis variance) analysis done out most influence factor