Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool

Abstract

AbstractHigh-quality machining is a crucial aspect of contemporary manufacturing technology due to the vast demand for precision machining for parts made from hardened tool steels and super alloys globally in the aerospace, automobile, and medical sectors. The necessity to upheave production efficiency and quality enhancement at minimum cost requires deep knowledge of this cutting process and development of machine learning-based modeling technique, adept in providing essential tools for design, planning, and incorporation in the machining processes. This research aims to develop a predictive surface roughness model and optimize its process parameters for ultra-precision hard-turning finishing operation. Ultra-precision hard-turning experiments were carried out on AISI D2 of HRC 62. The response surface method (RSM) was applied to understand the effect of process parameters on surface roughness and carry out optimization. Based on the data gained from experiments, machine learning models and algorithms were developed with support vector machine (SVM), Gaussian process relation (GPR), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) for the prediction of surface roughness. The results show that all machine learning models gave excellent predictive accuracy with an average MAPE value of 7.38%. The validation tests were also statistically significant, with ANFIS and ANN having MAPE values of 9.98% and 3.43%, respectively. Additional validation tests for the models with new experimental data indicate average R, RMSE, and MAPE values of 0.78, 0.19, and 36.17%, respectively, which are satisfactory. The RSM analysis shows that the feed is the most significant factor for minimizing surface roughness Rɑ, among the process parameters, with 92% influence, and optimal cutting conditions were found to be cutting speed = 100 m/min, feed = 0.025 mm/rev, and depth of cut = 0.09 mm, respectively. This finding can be helpful in the decision-making on process parameters in the precision machining industry.