Bibliographic record
Abstract
Temper bead welding has been developed by the judicious positioning of weld beads and the control of heat input with the objective of reduce the peak hardness within the weld HAZ and ultimately improve the fracture toughness. This is usually done by experiment, i.e., trial and error. This paper describes a computational weld mechanics model to compute the transient temperature and transient microstructure evolution in temper bead welds. It is shown that the micro-structure in welds in low-alloy steels can be computed with useful accuracy and resolution for multi-pass welds. Furthermore by combining this analysis with a Design of Experiment (DOE) methodology, this approach has the potential to optimize the design of temper bead weld procedures. Comparison with experimental measurements are used to validate the model. Because hardness can be measured easily, quickly and economically, there is a particular focus on hardness as the most important criterion required in practice. Hardness is a function of carbon equivalent, austenite grain size and cooling rate between 800 to 600 °C. The micro structure can not be characterized by the power per unit length of weld or heat input. This is shown by running tests with different values of speed and current while the heat input is kept unchanged. On the other hand, the parameters in power are not fixed in practice and vary along the weld. This motivates a DOE perturbation analyses for two main parameters in the power; current and speed. The result shows the upper and lower bound expected for hardness due to the variations of each parameter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".