Calculation of thermal features in welding and additive manufacturing
Bibliographic record
Abstract
Abstract This paper summarizes multiple engineering expressions that enable the prediction of thermal magnitudes of interest associated with moving heat sources. The expressions use only fundamental parameters available before performing any experiments, and their calculation is algebraic, without the need for numerical methods. All expressions are based on the fundamental governing equations of heat transfer in the solid. The magnitudes predicted include maximum width and its location, maximum penetration, thickness of HAZ, maximum temperature and its location, leading and trailing edge of an isotherm, heating and cooling rate, aspect ratio of an isotherm, melting efficiency, cooling time from 800°C to 500°C, solidification time, and maximum distribution of a heat source to reach a target temperature. Parameters involved include heat source power and speed, thermal conductivity and diffusivity of the substrate material, temperature of interest and preheat or interpass temperature. Temperature-dependent properties are accounted for by the use of effective properties. The expressions proposed can be extended into sophisticated geometries for welding and specific additive manufacturing cases.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".