Reconceptualizing the Prognostics Digital Twin for Smart Manufacturing with Data-Driven Evolutionary Models and Adaptive Uncertainty Quantification

Abstract

This work presents an integrated architecture for a prognostic digital twin for smart manufacturing subsystems. The specific case of cutting tool wear (flank wear) in a CNC machine is considered, using benchmark data sets provided by the Prognostics and Health Management (PHM) Society. This paper emphasizes the role of robust uncertainty quantification, especially in the presence of data-driven black- and gray-box dynamic models. A surrogate dynamic model is constructed to track the evolution of flank wear using a reduced set of features extracted from multi-modal sensor time series data. The digital twin's uncertainty quantification engine integrates with this dynamic model along with a machine emulator that is tasked with generating future operating scenarios for the machine. The surrogate dynamic model and emulator are combined in a closed-loop architecture with an adaptive Monte Carlo uncertainty forecasting framework that allows prediction of quantities of interest critical to prognostics within user-prescribed bounds. Numerical results using the PHM dataset are shown illustrating how the adaptive uncertainty forecasting tools deliver a trustworthy forecast by maintaining predictive error within the prescribed tolerance.