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Machine learning for predicting laser ablation groove characteristics in polycrystalline diamond

At a Glance

Section titled ā€œAt a Glanceā€
MetadataDetails
Publication Date2024-04-01
JournalJournal of Physics Conference Series
AuthorsRuiwang Tan, Zhanjiang Yu, Yiquan Li, Jinkai Xu
InstitutionsChangchun University of Science and Technology

Abstract

Section titled ā€œAbstractā€

Abstract This paper explores machine learningā€™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 modelsā€”Elastic Net, Random Forest, Gradient Boosting Regression, XGBoost Regression, Bayesian Regression, and Gaussian Process Regressionā€”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.

Tech Support

Section titled ā€œTech Supportā€

Original Source

Section titled ā€œOriginal Sourceā€

References

Section titled ā€œReferencesā€
  1. 2018 - Preparation technology and properties of microtexture diamondcoated tools [Crossref]
  2. 2021 - State of the art of tool texturing in machining
  3. 2019 - An integrated multi response Taguchi-neural network robust data envelopment analysis model for CO2 laser cutting
  4. 2015 - Optimization of laser cutting parameters for Al6061/SiCp/Al2O3 composite using grey based response surface methodology (GRSM), Meas
  5. 2022 - Experimental investigations of channel profile and surface roughness on PMMA substrate for microfluidic devices with mathematical modelling
  6. 2020 - On hyperparameter optimization of machine learning algorithms: Theory and practice
  7. 2000 - Cross-validation methods

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