About this Abstract |
| Meeting |
MS&T26: Materials Science & Technology
|
| Symposium
|
Computational Materials for Qualification and Certification
|
| Presentation Title |
Towards a Predictive Modeling Platform for Fatigue in Additively Manufactured Metals |
| Author(s) |
Somnath Ghosh, Lucas Ferreira, Nolan Craig McGee Strauss, Prajwal Arunachala, Anthony D. Rollett |
| On-Site Speaker (Planned) |
Somnath Ghosh |
| Abstract Scope |
Predicting fatigue life in additively manufactured parts with location-dependent material microstructures and properties is a challenging undertaking. This talk will discuss steps towards the development of a multiscale predictive platform for early stages of fatigue in LPBF Ti-6Al-4V alloys. Ingredients of an efficient parametrically-upscaled constitutive model (PUCM) and fatigue crack growth model, bridging micro and macro length scales through the explicit representation of the statistics of microstructure and defect morphology and crystallography, in the form of representative aggregated microstructural parameters (RAMPs), will be addressed. Image-based microstructural crystal plasticity-phase field models of deformation and fatigue crack growth, incorporating the statistics of alpha laths in parent beta grains, are first developed to create a microstructure response database (MRDB). Genetic programming symbolic regression (GPSR) tools subsequently operate on the MRDB to generate PUCM parameters as explicit functions of RAMPs. The model will explore the role of defects and microstructure on part-level damage evolution. |