Thermal modeling of DED repair process for slender panels by a 2D semi-analytic approach
Bibliographic record
Abstract
Directed Energy Deposition is one of the leading additive manufacturing technologies tailored for the repair of metallic components. The spatial and temporal pattern of the heat flux results in specific thermal gradients and cooling rates, controlling the final microstructure and mechanical properties of the repaired component. Simplified thermal analyses based on Rosenthal's solution offers an interesting way to model in short computational times the repair process of simple geometries, estimating the spatial thermal gradients or cooling rates. This article presents a new model based on Rosenthal's solution. Compared to other existing analytic solutions, the present work contains material layer addition and therefore enables the modeling of not only one layer but of the complete additive manufacturing process. The validity domain of the model is identified using experimental measurements on 316L stainless steel. Possible applications are also provided: determination of solidification regime (columnar or equiaxed grains) in solidification maps or optimization of the duration of interlayer dwell time needed to keep the part under a low annealing temperature.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".