| Original language | English |
|---|---|
| Pages | 193-198 |
| Number of pages | 6 |
| State | Published - 2021 |
| Event | 36th Annual Meeting of the American Society for Precision Engineering, ASPE 2021 - Minneapolis, United States Duration: Nov 1 2021 → Nov 5 2021 |
Conference
| Conference | 36th Annual Meeting of the American Society for Precision Engineering, ASPE 2021 |
|---|---|
| Country/Territory | United States |
| City | Minneapolis |
| Period | 11/1/21 → 11/5/21 |
Funding
This research was supported by MxD project number 20-11-04 Physics-Guided Machine Learning (PGML) for CNC Milling. This material is based on research sponsored by Office of the Under Secretary of Defense for Research and Engineering, Strategic Technology Protection and Exploitation, Defense Manufacturing Science and Technology Program under agreement number W15QKN-19-3-0003 between MxD and the Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes. 2 Notice: 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 (http://energy.gov/downloads/doe-public-access-plan).