Reconfigurable model clusters for scalable modelling of feed drive dynamics

This paper presents a novel approach to modeling feed drive dynamics in machine tools. Instead of building and calibrating separate models for each machine, this approach leverages a cluster of pre-calibrated models to represent a fleet of similar machines or a single machine under varying conditions. Bayesian model selection assimilates the internal controller signals into the model cluster, selecting an optimal combination of the models to represent individual machines accurately. This approach facilitates the large-scale development of machine tool digital twins and shadows without additional modeling or experimental calibration. The effectiveness of the proposed approach is demonstrated through numerical simulations with known ground truths. The results highlight the concept's potential to simplify and scale feed drive modeling. Additionally, they outline the key technical and operational considerations necessary for its broader application.