Physics-informed Bayesian machine learning for probabilistic inference and refinement of milling stability predictions

Abstract

This paper proposes an industrial-friendly approach based on physics-informed Bayesian machine learning for probabilistic inference and refinement of stability predictions in milling. The knowledge from physics principles and theoretical models is gathered into a substructure-based framework and enables dedicated updating and exchange of models on a substructure level. The Bayesian learning algorithm utilizes this framework as a basis to simultaneously interact with multiple machine and process configurations that share substructures, leading to federated and transfer learning over a network of machine tools on a production site. Moreover, the paper further elaborates on the Bayesian perspective to anticipate the potential usefulness of given experimental data in improving the model accuracy by quantifying information gain for two primary purposes: Firstly, recursive online learning can manage computational resources by processing only the most informative data points that may be collected during arbitrary cuts. Secondly, active learning leverages the information gain to dynamically adapt a sequential data collection, leading to accelerated learning with minimal labeled data. The experimental validations confirm that the proposed approaches lead to effective learning and reliable predictions of milling stability, outperforming traditional deterministic methods.