Robust Model for Fatigue Life Estimation from Monotonic Properties Data for Steels
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Determining the fatigue properties (Manson-Coffin and Ramberg-Osgood parameters) for a steel material requires time consuming and expensive testing. In the early stages of a design process, it is not feasible to perform this testing. To help solve this problem numerous researchers have developed estimation methods to estimate the Manson-Coffin parameters from monotonic properties data. Additionally, other researchers have compared the results from these various estimation methods for large material classifications. However, a comprehensive comparison of these estimation methods has not been made for steels in different heat treatment states. More accurate results for the best estimation method can be made with smaller classifications, which have more consistent properties. In this research, best estimation methods are determined for six steel heat treatments. In addition to looking at steel heat treatment classifications, the estimation of the Ramberg-Osgood parameters is also examined through the compatibility conditions. Without them, the approach of estimating the fatigue properties using the estimation methods would not be practically useful. Finally, in the comparison of the estimation methods, an appropriate statistical comparison methodology is utilized; multiple contrasts comparison. This methodology is implemented into the comparison of the different estimation methods, by comparing the estimated lives and the experimental lives as a regression so that the entire life range can be considered. The estimation methods can also be utilized to get estimates of the variability of the fatigue properties given the variability of the monotonic properties data, since there is a functional relationship developed between the two sets of material properties. This variability is necessary for a stochastic design process, in order to obtain a more optimally designed component or structure. Overall the estimation methods have a number of practical applications within a fatigue design process. Their use and implementation needs to be supplemented by the appropriate knowledge of their limitations and for what classifications they give the best results. An expert system is developed to summarize this knowledge to assist an engineer. This research aims to provide this knowledge and expands their use to account for variability in fatigue properties for stochastic analysis.