|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Additive Manufacturing Fatigue and Fracture: Developing Predictive Capabilities
||Probabilistic Methods for Additive Manufacturing – How to Understand and Manage the Uncertainty
||Mohsen Seifi, Martin White, Mahdi Jamshidinia, Doug Wells
|On-Site Speaker (Planned)
The AM process exhibits variability throughout, from controlling the feedstock to the effects of defects and subsequent influence on fatigue and fracture behavior. In particular, some of the flaws are related to process escapes (e.g., powder short-feed or recoater issues, residual stress cracking, etc.) and can be considered as rogue. As such, there is an enormous opportunity to deploy probabilistic methods to help quantify and manage the uncertainty in the process. The ASTM International AM CoE is currently working with NASA to build a probabilistic framework that can be used to manage uncertainty and support future certification methods. This presentation will provide an overview of these probabilistic approaches for both printing of the material, as well as the evaluation in terms of structural integrity lifing models. A key topic will consider the likelihood of rogue defect occurrence, as well as the probability of defect detection using available Non-Destructive Testing methods.
||Additive Manufacturing, Other,