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



Published Oct 26, 2023
Jack Murray Brandon Chamberlain Nicholas Hemleben Daniel Ospina-Acero Indranil Nayak Andrew VanFossen Frank Zahiri Mrinal Kumar


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.

How to Cite

Murray, J., Chamberlain, B., Hemleben, N., Ospina-Acero, D., Nayak, I., VanFossen, A., Zahiri, F., & Kumar, M. (2023). Reconceptualizing the Prognostics Digital Twin for Smart Manufacturing with Data-Driven Evolutionary Models and Adaptive Uncertainty Quantification. Annual Conference of the PHM Society, 15(1).
Abstract 1402 | PDF Downloads 258



Prognostic digital twin, Monte Carlo forecasting, Dynamics learning, Smart manufacturing, Uncertainty quantification

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