Novel machine learning based models for estimating minimum surface roughness value in the end milling process

Average surface roughness value (R_a) is an important measure of the quality of a machined work piece. Lower the R_a value, the higher is the work piece quality and vice versa. It is therefore desirable to develop mathematical models that can predict the minimal R_a value and the associated machining conditions that can lead to this value. In this paper, real experimental data from an end milling process is used to develop models for predicating minimum R_a value. Two machine learning techniques, Model Tree and Sequential Minimal Optimization based Support Vector Machine, which have not been used before to model surface roughness, were applied to the training data to build prediction models. The developed models were then applied to the test data to determine minimum R_a value. Results indicate that both techniques reduced the minimum R_a value of experimental data by 4.2% and 2.1% respectively compared to the previously reported values. Model trees are found to be better than other approaches in predicting minimum R_a value.