About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Purveyors of Processing Science and ICME: A SMD Symposium to Honor the Many Contributions of Taylan Altan, Wei Tsu Wu, Soo-Ik Oh, and Lee Semiatin
|
Presentation Title |
Optimizing Metals Additive Manufacturing |
Author(s) |
Aaron Stebner |
On-Site Speaker (Planned) |
Aaron Stebner |
Abstract Scope |
The Alliance for the Development of Additive Processing Technologies (ADAPT) is developing a combined physics – machine learning platform for assessing Process-Structure-Property relationships in metals additive manufacturing. In this presentation, we will show how such a framework can be used to optimize parts, processes, and materials for additive manufacturing, resulting in reduced times and costs for qualifications. The parts example will cover rapid qualification of a 17-4 Stainless Steel door hinge for an Army ground vehicle. The materials example will document ways and means to make laser powder bed fusion manufactured Inconel 718 stronger & more ductile than wrought material. The processes example will show how machine learning can be used to determine the parameters for a new printer that has a more powerful laser than previous generations of machines, with verification carried out in printing Ti-64 coupons. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |