Detection and quantification of creep strain using process compensated resonance testing (PCRT) sorting modules trained with modeled resonance spectra.

Autor: Heffernan, Julieanne, Biedermann, Eric, Mayes, Alexander, Livings, Richard, Jauriqui, Leanne, Goodlet, Brent, Aldrin, John C., Mazdiyasni, Siamack, Chimenti, Dale E., Bond, Leonard J.
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Zdroj: AIP Conference Proceedings; 2018, Vol. 1949 Issue 1, pN.PAG-N.PAG, 10p
Abstrakt: Process Compensated Resonant Testing (PCRT) is a full-body nondestructive testing (NDT) method that measures the resonance frequencies of a part and correlates them to the part’s material and/or damage state. PCRT testing is used in the automotive, aerospace, and power generation industries via automated PASS/FAIL inspections to distinguish parts with nominal process variation from those with the defect(s) of interest. Traditional PCRT tests are created through the statistical analysis of populations of “good” and “bad” parts. However, gathering a statistically significant number of parts can be costly and time-consuming, and the availability of defective parts may be limited. This work uses virtual databases of good and bad parts to create two targeted PCRT inspections for single crystal (SX) nickel-based superalloy turbine blades. Using finite element (FE) models, populations were modeled to include variations in geometric dimensions, material properties, crystallographic orientation, and creep damage. Model results were verified by comparing the frequency variation in the modeled populations with the measured frequency variations of several physical blade populations. Additionally, creep modeling results were verified through the experimental evaluation of coupon geometries. A virtual database of resonance spectra was created from the model data. The virtual database was used to create PCRT inspections to detect crystallographic defects and creep strain. Quantification of creep strain values using the PCRT inspection results was also demonstrated. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index