Enhancement of a quasi-analytical solution for modelling additive manufacturing processes
Bibliographic record
Abstract
Abstract Numerical modelling methods (e.g. finite element) can provide accurate descriptions of long-range temperature fields in laser or electron-beam melting processes, however the high computational costs at part-scale make them unsuitable for process modelling in additive manufacturing (AM). Alternative methods such as semi-analytical solutions based on a moving heat source reduce the computational expense but at the cost of unrealistic assumptions. Radiation, temperature-dependent physical properties and latent heat are not considered in the semi-analytical approach but can have a significant effect on the thermal history. In this study, the error associated with each of these contributions are assessed against the conduction-only semi-analytical solution for a range of processing parameters for surface melting on solid Ti-6Al-4V. The semi-analytical model is then “enhanced” using results from finite element simulations to better account for the heat transfer in the AM process.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".