Surface strain measurements using image correlation require a pattern to be applied to the surface of the object being measured. Lithography, the most widely used method for repeatable patterning is expensive, requiring dedicated technical staff and significant infrastructure. Lithography is time consuming, often requiring several days for each patterning application, which limits throughput. An innovative method has been developed and tested whereby repeatable patterns for image correlation are applied without dedicated technical staff or special infrastructure and can be completed in a few minutes rather than days. This new method is more amenable to application of patterns to complex surface geometries and larger surface areas. The new micro stamping method allows for higher contrast patterning materials, which improves the accuracy of strain measurements using image correlation.Accurate surface strain measurements using image correlation are dependent on the application of a high-contrast pattern to the surface of the object being measured. Error in the strain measurement is dependent on the particular pattern applied, and repeatability of the pattern on various surfaces is ideal. Micro texture stamping is a repeatable, high throughput, high-resolution, low cost, parallel patterning method in which a stamp surface pattern is replicated into a material by mechanical contact. Details of the flexible micro textured stamps produced by 1900 Engineering have been published [1][2]. The stamps were fabricated with a 10 µm base-element size. The electron-beam lithography (EBL) process for generating the stamp master took 57 hours to complete a 12.7 mm x 12.7 mm area using an e-beam resist [3]. Without the stamping procedure, EBL would need to be repeated for each subsequent specimen to be patterned; however, after fabricating the stamp, the pattern application took approximately 10 minutes per subsequent specimen.A diagram for the stamp usage is in Figure 1. The procedure for application of the stamp to create a speckle pattern is: (1) Sonicate the specimen in acetone, then methanol, and dry; (2) With a fine liner, apply MCC primer on clean specimen; (3) Let stand for 15 s and gently apply compressed air from the top; (4) Bake for 3 min at 115 ºC on a hot plate, then remove the specimen. (5) Let the hot plate cool to 60 ºC, (6) Place two specimens side by side (to allow level stamping -the procedure can be applied to one specimen); (7) Apply Shipley 1805 photo resist on one specimen; (8) Wait 20 s; (9) Apply the Shipley in the same specimen; (10) Align the stamp by touching first the dummy specimen and then let the stamp lay down over the specimen to be stamped. (11) Adjust the hot plate for 115 ºC; (12) Place a piece of cook paper over the stamp, to allow a non-stick surface for the weight; (13) Apply weight (~4 psi); (14) Bake for 8 minutes; (15) Remove the weight and the specimen from the hot plate; (16) Carefully peel the stamp off the specimen; (17) Check the gauge section on the microscope for the patterns. The...
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.
Uncertainty quantification and propagation form the foundation of a prognostics and health management (PHM) system. Particle filters have proven to be a valuable tool for this reason but are generally restricted to state-space damage models and lack a natural approach for quantifying model parameter uncertainty. Both of these issues tend to inhibit the real-world application of PHM. While Markov chain Monte Carlo (MCMC) sampling methods avoid some of these restrictions, they are also inherently serial, and, thus, MCMC can become intractable as model fidelity increases. Over the past two decades, sequential Monte Carlo (SMC) methods, of which the particle filter is a special case, have been adapted to sample from a single, static posterior distribution, eliminating the state-space requirement and providing an alternative to MCMC. Additionally, SMC samplers of this type can be run in parallel, resulting in drastic reductions in computation time. In this work, a potential path toward real-time, highfidelity prognostics using a combination of surrogate modeling and a parallel SMC sampler is explored. The use of SMC samplers to enable tractable parameter estimation for full-fidelity (i.e., non-surrogate-assisted) damage models is also discussed. Both of these topics are studied in the context of fatigue crack growth in a geometrically complex, metallic specimen subjected to variable amplitude loading.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.