Evaluation of automated stability testing in machining through closed-loop control and Bayesian machine learning

•A system for automated identification of the optimal stable cutting parameters in milling through Bayesian machine learning and closed-loop control is described.•Experimental results demonstrate that the system can identify the optimal stable parameters without information about the cutting force model or the structural dynamics in an automated way.•Multiple strategies for test parameter selection are described and evaluated.•Methods for implementing closed-loop control in different commercial machine tool controllers are described.•The system provides a low-cost method for optimal stable parameter identification in an industrial environment.