|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||Fatigue in Materials: Fundamentals, Multiscale Modeling and Prevention
||Toward the Use of Machine Learning to Understand and Predict Microstructurally Small Fatigue-Crack Evolution
||Nathan Wilkinson, Brian Phung, Jacob Hochhalter, Ashley Spear
|On-Site Speaker (Planned)
Improving structural prognosis capabilities for fatigue-critical components requires accurate rules for predicting growth of microstructurally small fatigue cracks (MSFCs). With simultaneous advancements in techniques for measuring and modeling material deformation and failure in 3D comes the challenge of post-processing, combining, and contextualizing large amounts of data from both experimental observations and numerical simulations. This talk will describe recent efforts to correlate, and to predict relationships among, intrinsic microstructural features, micromechanical fields, and crack-growth behavior within local neighborhoods along 3D crack fronts in a polycrystalline Al-Mg-Si alloy. Supervised machine learning is employed to map microstructural and micromechanical parameters to observed fatigue-crack growth rate and direction for discrete points along measured crack fronts. A non-trivial portion of the work involves parameterizing the descriptions of both microstructural and micromechanical neighborhoods as well as crack growth in three dimensions.
||Planned: Stand-alone book in which only your symposium’s papers would appear (indicate title in comments section below)