Machine learning cutting force, surface roughness, and tool life in high speed turning processes - Zhang, Yun; Xu, Xiaojie (Manufacturing letters, 2021)
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.