Abstract
Material produced via additive manufacturing (AM) continues to exhibit variable mechanical properties despite apparent optimization of processing parameters, inhibiting qualification efforts and limiting use in critical applications. Stochastic lack-of-fusion flaws may help explain this variability, but the origin of these seemingly random defects has to this point remained unclear. In this work, we show that spatter particles, material ejected from the laser melt pool, are directly responsible for generating stochastic lack-of-fusion in laser-based powder bed fusion components through the application of spatial statistics. A statistically significant, causal relationship between spatter particles and stochastic lack-of-fusion is established, and the spatial and morphological relationships between spatter and internal flaws are investigated. The occurrence of spatter-induced lack-of-fusion in relation to the inert gas flow and laser trajectory direction is also investigated, and recommendations for mitigating the occurrence of spatter are evaluated.
| Original language | English |
|---|---|
| Article number | 103298 |
| Journal | Additive Manufacturing |
| Volume | 61 |
| DOIs | |
| State | Published - Jan 5 2023 |
Funding
This research was sponsored by the US Department of Energy’s Advanced Manufacturing Office with support from Raytheon Technologies Corporation . 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 ). The authors would like to thank Joseph Simpson for his help with powder characterization, and the team at ZEISS Industrial Metrology, LLC., including Paul Brackman, Dr. Pradeep Bhattad, and Dr. Curtis Frederick, for their help during XCT data generation. The authors would also like to thank Dr. Alex Plotkowski for his thoughtful insights upon technical review of this document. This research was sponsored by the US Department of Energy's Advanced Manufacturing Office with support from Raytheon Technologies Corporation. 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). The authors would like to thank Joseph Simpson for his help with powder characterization, and the team at ZEISS Industrial Metrology, LLC. including Paul Brackman, Dr. Pradeep Bhattad, and Dr. Curtis Frederick, for their help during XCT data generation. The authors would also like to thank Dr. Alex Plotkowski for his thoughtful insights upon technical review of this document. Notice of Copyright: 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 ).
Keywords
- Additive Manufacturing
- Flaws
- Process monitoring
- Spatial statistics
