Accurately predicting cutting forces in hard turning processes can lead to improved process control, reduced tool wear, and enhanced productivity. This study aims to predict machining force components during the hard turning of AISI 52100 bearing steel using machine learning models. Specifically, eight models were considered, and their prediction performance was assessed using experimental data collected during AISI 52100 bearing steel turning with a CBN cutting tool. The fivefold cross-validation technique has been adopted in training to obtain more reliable estimates of the performance of a model and reduce the risk of overfitting the data. Results showed that the Gaussian process regression (GPR) and decision tree regression outperformed the other models, with averaged root-mean-square error values of 14.44 and 12.72, respectively. GPR also provided prediction uncertainty. Additionally, feature selection was performed using two algorithms, namely Regressional Relief-F and F test, to identify the most important features impacting the cutting forces. The findings of this study can be useful in optimizing cutting parameters for hard turning processes to select cutting forces, reduce tool wear, and minimize the generated heat during the machining process.
Bibliographical noteKAUST Repository Item: Exported on 2023-09-06
ASJC Scopus subject areas
- Mechanics of Materials
- Materials Science(all)
- Mechanical Engineering