In situ heat treatment in laser beam welding: a study on high-temperature processing of low-carbon steels
Bibliographic record
Abstract
Pre- or post-heating is commonly employed during welding. Inducing heat to the joint prior to welding is known to be an efficient way of reducing susceptibility to cracks and improving the toughness of the weld metal. In laser beam welding (LBW), the use of pre- or post-heating is less common, mainly because of the high productivity and restrictions on access to the small molten pool. However, in situ heat treatments during LBW are very common in the steel industry when joining hot-rolled sheets before coiling. Nevertheless, the implications of heating and cooling routines during LBW are still not fully understood, and studying them is important for industrial applications. This study intends to contribute to this discussion by reviewing some phenomena associated with high-temperature laser beam welding (HTLBW) and examining a case study of low-carbon steels. Sheets of low-carbon enhanced-plasticity steel were subjected to in situ LBW heat treatment in accordance with their chemical composition, thermomechanical treatment, and proposed use. Because this steel required rolling after welding, a ferritic microconstituent instead of martensite provided superior workability to the product. For some other classes of steels, there is an advantage in processing flat products at high temperatures, depending on the case, which was reviewed in this study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".