Discrete Element Method framework to simulate metallic Laser Powder-Bed Fusion additive manufacturing process
Bibliographic record
Abstract
The present work introduces a Discrete Element Method (DEM) framework to simulate metallic Laser Powder-Bed Fusion (L-PBF) additive manufacturing process.This latter is expected to take into account the main steps of additive manufacturing from the 3D printing G-code to the characterization of printed parts before postprocessing through the simulation of laser/powder bed thermo-mechanical interaction and the determination of residual stresses and distortions.In this paper, a 3-step investigation is led to validate and exploit the developed DEM-based methodology.For validation purposes, we first consider a reference problem to compare melt pool geometrical and thermal characteristics given by Gusarov radiation model with finite element results coming from the literature.Then, we simulate the 3D printing of a simple geometric part.Finally, we exploit the developed approach to determine the influence of laser parameters in this case and more complex configurations.Results highlight the ability of DEM to reproduce L-PBF process and provide crucial information as temperature fields for optimisation purposes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".