| About this Abstract |
| Meeting |
2019 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing of Metals: Fatigue and Fracture III
|
| Presentation Title |
A Statistical Framework to Qualify the Low Cycle Fatigue Performance of Additively Manufactured Steel Replacement Parts |
| Author(s) |
Aaron Stebner |
| On-Site Speaker (Planned) |
Aaron Stebner |
| Abstract Scope |
The Alliance for the Development of Additive Processing Technologies (ADAPT) is developing and validating statistical platforms of Process-Structure-Property models for Additive Manufacturing. We are coupling the latest advancements in Machine Learning together with state of the art Cloud-based computing and materials database infrastructure. In this presentation, we will report on work to demonstrate a statistical framework for qualifying replacement door hinges for Maxxpro ground vehicles for the Army, using pre-existing data sets to guide and accelerate the qualification process. We will also report on the fundamental differences in failure mechanisms between the legacy 1045 cold rolled steel hinge pins and the replacement 17-4 stainless AM pins in combined tension-torsion low cycle fatigue. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |