Machine learning cutting force, surface roughness, and tool life in high speed turning processes

Machine learning approaches can serve as powerful tools in machining optimization processes. Model performance, including accuracy, stability, and robustness, are major criteria to choose among different methods. Besides, the applicability, ease of implementations, and cost-effectiveness should be c...

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Bibliographic Details
Published inManufacturing letters Vol. 29; pp. 84 - 89
Main Authors Zhang, Yun, Xu, Xiaojie
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.08.2021
Subjects
Cutting parameters
Gaussian process regression
Machine learning
Machining
Turning
Gaussian process regression
Turning
Cutting parameters
Machining
Machine learning
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Summary:Machine learning approaches can serve as powerful tools in machining optimization processes. Model performance, including accuracy, stability, and robustness, are major criteria to choose among different methods. Besides, the applicability, ease of implementations, and cost-effectiveness should be considered for industrial applications. In this study, we develop Gaussian process regression models to predict three cutting parameters, the cutting force (Fc), surface roughness (Ra), and tool lifetime (T), in high speed turning processes based on the cutting speed (vc), feed rate (f), and depth of cut (ap). The models are highly stable and accurate, and are thus promising as fast, robust, and low-cost approaches for cutting parameter estimations.
ISSN:2213-8463
2213-8463
DOI:10.1016/j.mfglet.2021.07.005