Doctor of Philosophy (PhD)


Mechanical Engineering

Document Type



All materials when subjected to fatigue loading are prone to failure if the number of cycles exceeds a certain level. Prediction of the number of cycles to failure is, therefore, of utmost importance in nearly all engineering applications. The existing methods for evaluating the fatigue life are tedious, expensive, and extremely time consuming as fatigue often takes many thousands to millions of cycles until failure occurs. Therefore, methods that can readily estimate the number of cycles to failure are highly desirable. In this work, innovative solutions to fatigue problems are presented and their practical significance is discussed. The premise of this research is that the energy dissipation due to hysteresis effect manifests itself as heat, raising the temperature of the specimen. The temperature evolution during fatigue can be utilized as an index to assess the useful fatigue life and fast prediction of premature failure. Specifically, fatigue experiments of two types of metals (Aluminum 6061-T6 and Stainless Steel 304L) show that the slope of the temperature at the beginning of the test is intimately related to the fatigue life, thereby, it provides a fast prediction technique to assess failure. An experimental investigation was conducted to study the effect of surface cooling on the improvement of fatigue life. Experiments show that the surface cooling has significant effect on the fatigue life. For example, 1000% improvement is observed for Steel 4145 undergoing rotating-bending fatigue. It is proposed that the concept of self-organization within the context of irreversible thermodynamics can be used to gain insight into the observed phenomenon. Further, it is shown that fatigue degradation and thermodynamic entropy generation are intimately connected and that their relationship can be used for prediction of failure and making fundamental advances in the study of fatigue without having to resort to traditional approaches that depend on empirical models.



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Committee Chair

Khonsari, Michael M.