Computationally efficient model to predict the evolution in the thermal field in the EB-PBF and LP-DED processes
Bibliographic record
Abstract
Abstract Understanding the development of a number of defects found in components fabricated by the metal Additive Manufacturing (AM) processes requires an understanding of the evolution in the thermal field within the component at both the macro- and meso-scales. As a first step, in this work, the agglomeration method was used in combination with a time-averaged input of energy to simulate the macro-scale evolution in temperature. Two example processes: 1) laser-based powder-fed directed energy deposition; and 2) electron beam powder bed fusion, are used to demonstrate the modelling methodology. The approach employed focuses on ensuring the conservation of heat and is applied using ABAQUS. The two applications have been validated by comparing the predicted thermal behaviour with process-derived data. The results indicate that this method is an efficient strategy to predict the thermal field at the scale of the component being fabricated.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".