Abstract
In powder bed fusion additive manufacturing, machines are often equipped with in-situ sensors to monitor the build environment as well as machine actuators and subsystems. The data from these sensors offer rich information about the consistency of the fabrication process within a build and across builds. This information may be used for process monitoring and defect detection; however, little has been done to leverage this data from the machines for more than just coarse-grained process monitoring. In this work we demonstrate how these inherently temporal data may be mapped spatially by leveraging scan path information. We then train a XGBoost machine learning model to predict localized defects—specifically soot–using only the mapped process data of builds from a laser powder bed fusion process as input features. The XGBoost model offers a feature importance metric that will help to elucidate possible relationships between the process data and observed defects. Finally, we analyze the model performance spatially and rationalize areas of greater and lesser performance.
| Original language | English |
|---|---|
| Article number | 767444 |
| Journal | Frontiers in Mechanical Engineering |
| Volume | 7 |
| DOIs | |
| State | Published - Nov 15 2021 |
Funding
This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). 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). Research was sponsored by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office. Copyright © 2021 Research was sponsored by the U.S. Department of Energy, Of fice of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office. The United States government retains and the publisher, by accepting the article for publication, acknowledges that the United States 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 United States government purposes. Halsey, Rose, Scime, Dehoff and Paquit. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
| Funders | Funder number |
|---|---|
| DOE Public Access Plan | |
| U.S. Department of Energy | |
| Advanced Manufacturing Office | |
| Office of Energy Efficiency and Renewable Energy |
Keywords
- additive manufacturing (3D printing)
- defect detection
- explainable AI
- machine learning
- process monitoring
- process-structure-property linkage
- spatio - temporal analysis
