This paper aims to provide a fatigue life prediction method that can concurrently approximate both SN behavior as well as the inherent variability of fatigue. Variability of fatigue is approximated efficiently with a limited number of experimental tests using a simplified energy based fatigue life prediction method or “Two Point” method. The purpose of such a tool is for the quality assessment and verification of resultant fatigue performance from Additive Manufacturing (AM) processes and other materials with limited information on material properties. Interest in AM technology is continually growing in many industries, such as aerospace, automotive, biomedical but AM built components often result in highly variable fatigue performance. The determination of optimal process parameters for the build process can be an extensive and costly endeavor due to either a limited knowledgebase or proprietary restrictions. Quantifying the significant variability of fatigue performance in AM components is a challenging task as there are many causes including machine to machine differences, recycling of powder, and process parameter selection.  Therefore, this paper proposes a life prediction method which can rapidly determine the fatigue performance of a material with little prior information of the material and a limited number of experimental tests as an aid in process parameter selection and fatigue performance qualification. This is performed by using a previously developed and simplistic energy based fatigue life prediction method, or Two Point method, to predict the inherent variability associated with fatigue performance. This assumption is verified by using predicted distributions of stress and cycles to failure and compared with experimental data at 10^4^ and 10^6^ cycles to failure.  SN life prediction curve fit parameters and confidence bounds of SN variability are evaluated using Bayesian statistical inference and stochastic sampling techniques for distribution estimation. This is performed in a dynamic way such that the life prediction model is continually updated with the generation of experimental data.


Rapid Characterization of Fatigue Performance With Application to Additive Manufactured Components

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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