The Digital Twin paradigm is based on the idea that a component’s serviceable life and performance can be better predicted and monitored by creating a faithful virtual counterpart of a real component, which in turn leads to improvements in end-product safety and cost. Such a model requires accurate inputs for the initial material state of the part as well as in-service loads and damage states throughout its service life. The resonance frequencies of a part correlate to a part’s material state and damage state. Similarly, changes in resonance frequencies correlate to changes in the part’s material state resulting from in-service loads and damage. Process Compensated Resonance Testing (PCRT) leverages these physical relationships to perform nondestructive evaluation (NDE) and material characterization using the measured resonance frequencies of a component. Prior work has established techniques for modeling the effects of material property variation, crystal orientation, and damage states on resonance, as well as quantifying uncertainty propagation from model inputs to outputs. This study examines the use of PCRT model inversion to obtain material properties and calibrate digital twins of real components. Digital twin instances were first created for a population of single crystal Nickel-base superalloy samples using dimension and mass measurements. Then, after collecting resonance spectra from the physical counterparts, model inversion techniques were employed to estimate elastic properties and crystal orientation for each part. The digital twins were then calibrated with the model inversion output. These digital twins were subsequently validated by comparing the inversion results to resonance and x-ray diffraction measurements on a statistically significant population of physical specimens. The results highlight the value of part-specific material properties for digital twin performance, as well as the ability of PCRT to evaluate and improve digital twin fidelity.
Process compensated resonance testing (PCRT) inversion for material characterization and digital twin calibration
Alexander Mayes, Julieanne Heffernan, Leanne Jauriqui, Richard Livings, Eric Biedermann, John C. Aldrin, Brent R. Goodlet, Siamack Mazdiyasni; Process compensated resonance testing (PCRT) inversion for material characterization and digital twin calibration. AIP Conf. Proc. 8 May 2019; 2102 (1): 020019. https://doi.org/10.1063/1.5099723
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