RESUMO
Digital image correlation (DIC) in a scanning electron microscope and high-angular resolution electron backscatter diffraction (HREBSD) provide valuable and complementary data concerning local deformation at the microscale. However, standard surface preparation techniques are mutually exclusive, which makes combining these techniques in situ impossible. This paper introduces a new method of applying surface patterning for DIC, namely a urethane microstamp, that provides a pattern with enough contrast for DIC at low accelerating voltages, but is virtually transparent at the higher voltages necessary for HREBSD and conventional EBSD analysis. Furthermore, microstamping is inexpensive and repeatable, and is more suitable to the analysis of patterns from complex surface geometries and larger surface areas than other patterning techniques.
RESUMO
This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model.
