DEM simulation to predict the powder bed quality for additive manufacturing processes
Bibliographic record
Abstract
Many metal additive manufacturing (AM) technologies, such as laser powder bed fusion (LPBF), rely on the stacking of thin powder layers with a rigid blade or a roller. The spreading step is critical because defects in the powder bed (i.e., low packing density, poor surface uniformity, particle segregation, etc.) often result in defects in the final product (i.e., lack of fusion, porosity, etc.). Currently, the lack of understanding of the spreading process, which depends, among other factors, on the powder properties and spreader geometry, results in a trial-and-error approach to ensure a quality powder bed which is not cost effective. Discrete Element Method (DEM) simulations can help us understand the mechanisms at play during the spreading process and select the best operating parameters (i.e., spreader geometry, spreader speed, layer thickness, etc.) for a given powder feedstock. In this work, we first present Lethe, an open-source DEM software capable of simulating the spreading of multiple powder layers, with periodically moving spreaders, powder dispenser platform and build-plate, all in a single simulation. We then use this software to run large-scale DEM simulations, evaluating the influence of the operation parameters and of the DEM parameters related to the feedstock (i.e., surface energy, restitution coefficient, friction coefficients, etc.) on the powder bed quality. Finally, through a collaboration with the National Research Council of Canada, we use an experimental set-up that replicates the whole spreading process while allowing freedom on operating parameters. This experimental set-up measures the relative density of the powder bed in between each layer. Using DEM simulation, we replicate these experimental results to assess the role of the powder property on the bed density and to gain insight into the physical mechanism affecting the bed quality in certain operating conditions. This paves the way for a digital twin for powder spreading that could swiftly identify powder bed quality from the powder property and the operating parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".