Abstract
While Additive Manufacturing promises to reshape the manufacturing landscape, challenges related to part, and process qualification hinder its widespread adoption. The Instance-Qualified approach seeks to qualify individual parts, even for processes with high variability, by leveraging the concept of a digital twin. This work proposes a scalable cyberphysical infrastructure to enable the construction and use of such digital twins. This work also introduces the concept of an Augmented Intelligence Relay, which allows Artificial Intelligence algorithms to predict component performance for a given application even when it is impractical to perform a large number of physical tests.
| Original language | English |
|---|---|
| Pages (from-to) | 28-32 |
| Number of pages | 5 |
| Journal | Manufacturing Letters |
| Volume | 31 |
| DOIs | |
| State | Published - Jan 2022 |
Funding
The authors thank the MDF team for supporting this work which is sponsored by the US Department of Energy’s Advanced Manufacturing Office and the Transformational Challenge Reactor program. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://energy.gov/downloads/doe-public-access-plan ). The authors thank the MDF team for supporting this work which is sponsored by the US Department of Energy's Advanced Manufacturing Office and the Transformational Challenge Reactor program. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://energy.gov/downloads/doe-public-access-plan).
Keywords
- Additive manufacturing
- Artificial intelligence
- Cyberphysical infrastructure
- Digital twin
- Instance-qualified
