Vision-Assisted Probabilistic Inference of Milling Stability through Fully Bayesian Gaussian Process

Abstract

This paper presents a physics-free Bayesian approach for the learning and inference of probabilistic stability charts in milling operations. The approach does not require any information from machine tool structural dynamics or cutting force coefficients, and the underlying learning algorithm can operate with limited training data. A Fully Bayesian Gaussian Process with distributions on its kernel hyperparameters is employed to enable information transfer between different machine and process configurations. The vision system further automates the detection of necessary dimensions from the tool–holder assembly in the machine’s tool magazine, further enhancing the applicability of the approach. Experiments demonstrated the effectiveness of this approach, offering great promise as an industry-friendly solution.