Process Damping Identification Using Bayesian Learning and Time Domain Simulation

Abstract

Process damping can provide improved machining productivity by increasing the stability limit at low spindle speeds. While the phenomenon is well known, experimental identification of process damping model parameters can limit pre-process parameter selection that leverages the potential increases in material removal rates. This paper proposes a physics-informed Bayesian method that can identify the cutting force and process damping model coefficients from a limited set of test cuts without requiring direct measurements of cutting force or vibration. The method uses time-domain simulation to incorporate process damping and provide a basis for test selection. New strategies for efficient sampling and dimensionality reduction are applied to lower computation time and minimize the effect of model error. The proposed method is demonstrated, and the identified cutting and damping force coefficients are compared to values obtained using machining tests and least-squares fitting.