Modern gas turbine component design applies much effort into prediction and avoidance of fatigue. Advances in the prediction of low-cycle fatigue (LCF) cracks will reduce repair and replacement costs of turbine components. These cracks have the potential to cause component failure. Regression modeling of low-cycle fatigue (LCF)test data is typically restricted for use over the range of the test data. It is often difficult to characterize the plastic strain curve fit constants when the plastic strain is a small fraction of the total strain acquired. This is often the case with high strength, moderate ductility Ni-base superalloys. The intent of this project is to identify the optimal technique for extrapolating LCF test results into stress amplitudes approaching the ultimate strength. The proposed method to accomplish this is by finding an appropriate upper and lower bounds for the cyclic stress-strain and strain-life equations. Techniques investigated include: monotonic test data anchor points, strain-compatibility, and temperature independence of the Coffin-Manson relation. A Ni-base superalloy (IN738 LC) data set with fully reversed fatigue tests at several elevated temperatures with minimal plastic strain relative to the total strain range was used to model several options to represent the upper and lower bounds of material behavior. Several high strain LCF tests were performed with stress amplitudes approaching the ultimate strength. An augmented data set was developed by combining the high strain data with the original data set. The effectiveness of the bounding equations is judged by comparing the bounding equation results with the base data set to a linear regression model using the augmented data set.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-4308 |
| Date | 01 January 2007 |
| Creators | Radonovich, David Charles |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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