Comparative Analysis of Neural Networks and Decision Trees for Roughness Prediction

Abstract

In the current context of the manufacturing industry, optimizing the cutting parameters to achieve a controlled surface roughness involves costly and time-consuming experimental efforts. The present study addresses this challenge by developing robust machine learning-based approximate functions for predicting surface roughness (Ra) resulting from toroidal milling on a five-axis CNC. The research includes an experimental design conducted under real production conditions on C45 steel. The relatively small experimental dataset was augmented, normalized, and then scripts were written for four prediction models: two artificial neural network architectures and two models based on decision trees. Their performance was analyzed based on MSE, RMSE, R2, and MRA metrics. The results obtained reveal significant differences between the models, highlighting solutions with high accuracy, excellent robustness, and superior generalization capacity for new data. The study highlights the high potential of prediction models in optimizing machining processes, providing an effective way to reduce costly physical experiments and increase productivity in industrial environments.