| About this Abstract |
| Meeting |
Materials Science & Technology 2019
|
| Symposium
|
Advanced Manufacturing, Processing, Characterization and Modeling of Functional Materials
|
| Presentation Title |
Machine Learning for Solving Metals AM Problems |
| 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 an Artificial Intelligence platform that is capable of learning physical Process-Structure-Property (PSP) 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. An added benefit of our approach is the inherent statistical core of our models – the AI platform is not only capable of learning the PSP relations of AM, but also providing statistical reports on part quality and variation in a manner conducive to certification. 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. We will conclude with a vision for how networks of 3D printers that share a common database can shift paradigms in manufacturing. |