Machine learning cutting forces in milling processes of functionally graded materials

Machine learning approaches can serve as powerful tools in the machining optimization process. Criteria, such accuracy and stability, are important to consider when choosing among different models. For industrial application, it also is essential balance cost, applicabilities, ease of implementations. Here, we develop Gaussian process regression models for predicting main cutting force (R) its components three directions coordinate system ( $$F_{x}$$ , $$F_{y}$$ $$F_{z}$$ ) based on two predictors: depth cut $$a_{p}$$ feed rate (f) milling processes functionally graded materials. The model performance shows high thus promising estimating component a fast, cost effective, robust fashion.