Machine learning for predicting laser ablation groove characteristics in polycrystalline diamond

Abstract

Abstract This paper explores machine learning’s role in predicting laser-machined micro-groove texture on Polycrystalline Diamond (PCD) surfaces. PCD has been used for manufacturing ideal cutting insert due to its exceptional attributes, including hardness and thermal conductivity. Surface micro-texturing enhances accuracy and tool lifespan through micro-textures on tool surfaces. Laser micromachining, especially for its precision and efficiency, stands out among methods. Six regression models—Elastic Net, Random Forest, Gradient Boosting Regression, XGBoost Regression, Bayesian Regression, and Gaussian Process Regression—are used to predict groove depth and width based on laser parameters like energy, defocus, and speed. Experiments involve a nanosecond laser system and a commercial PCD tool. Results indicate both Gradient Boosting and XGBoost excel in predicting micro-groove texture. XGBoost slightly outperforms, credited to its enhancements over Gradient Boosting. This paper concludes that machine learning models, especially XGBoost and Gradient Boosting, effectively forecast micro-groove features on laser-machined PCD surfaces, offering insights for further research and practical applications in this domain.