Term of Award
Spring 2011
Degree Name
Master of Science in Applied Engineering (M.S.A.E.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mechanical Engineering
Committee Chair
Brian L. Vlcek
Committee Member 1
David Williams
Committee Member 2
Aniruddha Mitra
Abstract
Since fatigue is probabilistic, trends observed in large populations of data are necessary to select materials, compare engineering designs, or establish preventative maintenance schedules. The generation of large experimental fatigue populations, however, is prohibitively time consuming and costly. As a solution a Weibull-based Monte Carlo simulation of fatigue life was developed based upon a failed "bin" model, and five billion fatigue lives were simulated. These fatigue lives were used to generate L10 lives. A model of confidence number was developed dependent upon statistically large samples of simulated L10 fatigue lives, and independent of a limited number of published curves. Using these simulated values, Confidence number figures were generated that deviated from 0.0% - 7.4% of previously published figures and were independent of confidence bands. Results differed as little as 1% from those determined graphically for experimental bearing data sets while graphical interpolation was eliminated.
Recommended Citation
McBride, Jacob, "Ranking of Fatigue Data Based upon Monte Carlo Simulated Confidence Number Figures" (2011). Electronic Theses and Dissertations. 772.
https://digitalcommons.georgiasouthern.edu/etd/772
Research Data and Supplementary Material
No
