About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Data-Driven Bayesian Model-Based Prediction of Fatigue Crack Nucleation in Ni-based Superalloys |
Author(s) |
Somnath Ghosh, George Weber, Maxwell Pinz |
On-Site Speaker (Planned) |
Somnath Ghosh |
Abstract Scope |
This paper develops a Bayesian inference-based probabilistic crack nucleation model for the Ni-based superalloy Ren'e 88DT under fatigue loading. A data-driven, machine learning approach is developed to identify the underlying mechanisms driving crack nucleation. An experimental set of fatigue-loaded microstructures is characterized near crack nucleation sites using SEM and EBSD images for correlating the grain morphology and crystallography to the location of crack nucleation sites. A concurrent multiscale model that embeds experimentally acquired polycrystalline microstructural RVEs in a homogenized material, is developed for fatigue simulations. The RVE domain is modeled by a crystal plasticity finite element (CPFE) model. A Bayesian classification method is introduced to optimally select the most informative state variable predictors of crack nucleation and constructs a near-Pareto frontier of models. From this principal set of state variables, a simple scalar crack nucleation indicator is formulated. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, ICME, Modeling and Simulation |