Abstract
Machine learning approaches can serve as powerful tools in the machining optimization process. Criteria, such as accuracy and stability, are important to consider when choosing among different models. For the industrial application, it also is essential to balance cost, applicabilities, and ease of implementations. Here, we develop Gaussian process regression models for predicting the main cutting force (R) and its components in three directions of the coordinate system (\(F_{x}\), \(F_{y}\), and \(F_{z}\)) based on two predictors: the depth of cut (\(a_{p}\)) and the feed rate (f) in milling processes of functionally graded materials. The model performance shows high accuracy and stability, and the models are thus promising for estimating the cutting force and its component in a fast, cost effective, and robust fashion.
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XX: formal analysis, visualization, software, investigation, methodology, writing—original draft, writing—review, YZ: conceptualization, data curation, investigation, methodology, writing—original draft, writing—review. YL: writing—review. YL: writing— editing.
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Xu, X., Zhang, Y., Li, Y. et al. Machine learning cutting forces in milling processes of functionally graded materials. Adv. in Comp. Int. 2, 25 (2022). https://doi.org/10.1007/s43674-022-00036-w
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DOI: https://doi.org/10.1007/s43674-022-00036-w