Date of Award
Doctor of Philosophy (PhD)
Department of Aeronautics and Astronautics
Ramana Grandhi, PhD
Ryan A. Kemnitz, PhD
Current fatigue life modeling techniques with respect to defects emphasize the dependence on the defect size with respect to fatigue life, but does not account for the effects of where a defect might be located. This research outlines a process to include defect location into the model analysis for a more precise prediction of the number of cycles to failure and where the finial failure could occur within a component. The focus is on a turbine blade structure using IN718 subjected to a pure vibratory load. The basic model predicts component life using a stress map from the frequency analysis of the developed Finite Element Model (FEM) and synthetically generated defect sizes and location. Test specimen printed in IN718 are used to create experimental data to validate the model parameters, defect distributions, and predictions. The proposed results will be a map denoting the critical locations that may cause failure and predictions of fatigue life when both defect size and location are taken into consideration.
DTIC Accession Number
Miller, Daniel G., "Finite Fatigue Life Prediction of Additively Manufactured Aircraft Engine Turbine Blade with Respect to Internal Defect Size and Location" (2022). Theses and Dissertations. 5533.