Role of trochoidal machining process parameter and chip morphology studies during end milling of AISI D3 steel

Abstract

This study aims at discovering the effect of the trochoidal loop spacing parameter on Surface Roughness (SR), Specific Cutting Energy (SCE) and Temperature (T) during flat end milling operations. Twenty experimental runs were conducted based on the face centered central composite design (CCD) of response surface methodology (RSM). Artificial Neural Network (ANN) prediction modelling was created using four learning algorithms such as Batch Back Propagation Algorithm (BBPA), Quick Propagation Algorithm (QPA), Incremental Back Propagation Algorithm (IBPA) and Legvenberg–Marquardt back propagation Algorithm (LMBPA). The results were compared based on the value of Root mean square (RMSE) obtained for each learning algorithm and it was identified that LMBPA model produced least RMSE value. The predictive LMBPA neural network model was found to be capable of better predictions of surface roughness, temperature and specific cutting energy within the trained range. The Genetic algorithm(GA) gives the optimum parameters for conformation test and they are cutting speed of 41\xa0m/min, feed rate of 136\xa0mm/min and trochoidal loop spacing of 1.3\xa0mm and error percentage between experimental and GA predicted values is 3.60% for surface roughness, 3.15% for specific cutting energy and 3.89% for temperature was found to be minimal. Scratches and serrated boundaries at both side of the chips were observed and laces, chip adhesion and side flow marks were found on machined surface.