Abstract
Modern data analytics was employed to understand and predict physics-based melt-pool formation by fabricating Ni alloy single tracks using powder bed fusion. An extensive database of melt-pool geometries was created, including processing parameters and material characteristics as input features. Correlation analysis provided insight for relationships between process parameters and melt-pools, and enabled the development of meaningful machine learning models via the use of highly correlated features. We successfully demonstrated that data analytics facilitates understanding of the inherent physics and reliable prediction of melt-pool geometries. This approach can serve as a basis for the melt-pool control and process optimization.
| Original language | English |
|---|---|
| Pages (from-to) | 972-978 |
| Number of pages | 7 |
| Journal | Science and Technology of Advanced Materials |
| Volume | 20 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 31 2019 |
Funding
This research was supported by the Industrial Strategic Technology Development Program [10077677] and the Technology Innovation Program [20000201]; funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea);Ministry of Trade, Industry and Energy [10077677,20000201]. The authors are grateful to Dr. J.-K. Hong of the Korea Institute of Materials Science (KIMS) and Dr. Y. Kim of KAMI Co. Ltd. for the sample fabrication throughout the project.
| Funders | Funder number |
|---|---|
| Ministry of Trade, Industry and Energy | |
| Korea Institute of Materials Science |
Keywords
- 106 Metallic materials
- 404 Materials informatics / Genomics
- Powder bed fusion (PBF) process
- correlation analysis
- machine learning
- melt-pool
- single track
