Physics-Informed Machine Learning-Based Chattering Prediction in Milling Process

Chattering is a self-excited vibration phenomenon that results in poor surface quality of the workpiece in machining process. Analytical prediction of chattering initiation requires exact knowledge of milling process dynamics. Machine learning (ML) classification tries to effectively incorporate measurement data for real-time chattering prediction and suppression control. This paper proposes a physics-informed ML-based approach by integrating a deep neural network model for off-line dynamic parameter identification and a long short-term memory model for online real-time chattering prediction. In the offline phase, we extract critical dynamics parameters that inform the subsequent online chattering prediction. Experiments are conducted to validate and demonstrate the chattering prediction design. The comparison with another ML-based chattering prediction method is also presented to confirm the superior performance and reliability of the proposed approach.