Prediction and control of structured porosity in laser powder bed fusion (LPBF) additive manufacturing using multi-scale modeling
Bibliographic record
Abstract
This study presents a comprehensive multi-scale modeling framework for predicting and controlling process-induced porosity in Laser Powder Bed Fusion (LPBF) additive manufacturing. Three numerical approaches are developed and evaluated: a conduction-based finite element model, a hybrid geometric model with melt pool dimensions, and a multiphysics model. The use of partially molten powder particles is a significant innovation that improves prediction accuracy for porosity and surface morphology, especially under low-energy and high-hatch-spacing conditions. Model validation is carried out using high-resolution CT scans, SEM imaging, and buoyancy-based porosity measurements. The conduction and hybrid models offer computational efficiency and are appropriate for macro- to meso -scale investigations, but they have limited ability to capture precise pore morphology. The multiphysics model, incorporating melt pool fluid flow, surface tension effects, and partially molten powder particles, achieved porosity prediction within 5 % deviation of CT-based experimental values and closely matches observed pore structures across a range of laser energy densities (11.6–28.5 J/mm 3 ). The study also maps process-structure interactions, emphasizing the impact of laser energy density and hatch spacing on pore properties. Overall, the proposed novel framework serves as a prediction and design tool for adjusting porosity in LPBF components to meet application-specific performance requirements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".