A geometry-based comprehensive heat source model for FE thermal simulation of laser directed energy deposition
Bibliographic record
Abstract
Laser Directed Energy Deposition (L-DED) is a distinctive manufacturing process known for its relatively high deposition rate, minimal waste, and ability to make complex geometries. Accurate prediction of the temperature distribution and thermal history during L-DED is crucial for estimating the microstructure, porosity, and mechanical properties of the fabricated parts. However, existing analytical and numerical models often fall short in accuracy due to overlooking the geometrical characteristics and shape of the deposition. To address this issue, a multi-step statistical/numerical analysis workflow is proposed to elucidate the thermal responses in L-DED deposited tracks. First, a data-driven predictive model using statistical methods was used to estimate the deposition geometry based on the key process parameters which are laser power (P), powder feed rate (F), and scanning speed (V). Next, the prediction results were implemented in a dynamic hybrid quiet/inactive elemental control scheme to capture the deposition process. Further, activated elements are subsequently analyzed thermally through a transient 3-D finite element (FE) heat source model accounting for heat flux from conduction, convection, and radiation. The laser beam’s energy follows a two-dimensional Gaussian distribution, while the heat flux over the actual deposition region, modeled as a quarter-ellipsoid with the predicted geometrical characteristics. This representation captures the actual projection of the laser beam on the deposition. The simulated melt pool depths and temperature showed excellent agreement with experimental measurements for L-DED depositions of Inconel 625 superalloy, exhibiting less than 10% deviation, thereby validating the proposed heat source model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".