|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||GAT-2017 (Gamma Alloys Technology - 2017)
||Microstructure-sensitive Computational Scheme for Fatigue Resistance of Gamma-TiAl TNM Alloys
||Adrienne Muth, Paul Kern, Aaron Tallman, Thomas Payne, Don Shih, Ben Smith, David L. McDowell
|On-Site Speaker (Planned)
TiAl alloys show potential for elevated temperature components in aerospace applications, owing to comparable strength and creep resistance but reduced density relative to Ni-base superalloys, resulting in significant weight savings. Due to higher volume fraction of beta phase, addition of niobium and molybdenum to create TNM facilitates use of conventional forging and processing methods, which eases commercial restrictions on fabrication. Understanding process-structure-property relationships is critical to developing TNM alloys. In the spirit of Integrated Computational Materials Engineering, microstructure-sensitive computational modeling efficiently represents these relationships for stiffness, strength and fatigue resistance to augment costly empirical methods. A Python-scripted pipeline generates statistical volume elements capturing the morphology of multiphase TNM microstructures. Two crystal plasticity models are implemented for finite element analyses of generated microstructures to simulate macroscopic and local responses, considering model parameter and model form uncertainty. Deformation mechanisms are explored via Fatigue Indicator Parameters computed using ensembles of statistical volume elements.
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