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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".