Brazing of high-strength steels: Recent developments and challenges
Bibliographic record
Abstract
Zinc-coated high-strength steels (HSS) and advanced high-strength steels (AHSSs) are widely employed in automobile body manufacturing owing to their impressive metallurgical and mechanical characteristics. However, acquiring defect-free and mechanically sound welding joints is still quite challenging due to the formation of various defects, namely porosity, loss of coating, and the evolution of undesired microstructural phases in both the heat-affected zone and the fusion zone. The higher heat input during conventional fusion welding processes tends to exacerbate these challenges. Brazing, sometimes referred to as weld-brazing, is a comparatively new joining process that offers the ability to join thin and zinc-coated steel sheets with a significantly lower heat input using a compatible lower melting temperature filler wire, has been proposed as an alternative to fusion-based joining techniques. However, under-matching, i.e., mechanically weaker brazing filler than that of the base metal, limits the widespread application of brazing. In this regard, several developments have been reported to overcome under-matching by changing the filler composition, coating composition, and joining methodology. This comprehensive review highlights the key challenges associated with steel-to-steel brazing, while offering a detailed survey of various methods that can be used to improve the performance of brazed joints.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".