|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Additive Manufacturing Fatigue and Fracture: Developing Predictive Capabilities
||Microstructure-based Fatigue Studies on Additive-manufactured Materials
||Jiadong Gong, Gary Whelan, Abhinav Saboo, Greg Olson
|On-Site Speaker (Planned)
Additive manufacturing (AM) offers an exciting new direction enabling the production of otherwise impossible to achieve components. A great deal of progress has been made in the development of AM materials using integrated computational materials engineering (ICME). However, fatigue remains a major hurdle for qualification of new AM materials. In the present work, ICME approaches for microstructure-sensitive fatigue modeling for AM Ti64 are presented. The ICME-based fatigue model was employed to consider structure-property linkages, accounting for such structural attributes as the microstructure of the printed material, including phase fractions, grain size, and most importantly, crystallographic texture, as well as internal defects such as subsurface porosity, and external defects such as surface roughness. The focus of this work is high-cycle fatigue. The fatigue model is applied to predict worst-case fatigue life using extreme value distributions of fatigue indicator parameters as a surrogate measure of fatigue crack driving forces near subsurface defects.
||Additive Manufacturing, Modeling and Simulation, Mechanical Properties