Joint formation mechanism and mechanical properties of laser brazed Zn coated steel under different defocusing conditions
Bibliographic record
Abstract
Laser brazing, producing a class-A joint surface in automotive, relies on the defocus control to manage laser heating mode and braze performance. This work demonstrates that the laser interaction mechanism transitioned from keyhole welding to conduction brazing as defocus distances increased from −20 to ≥18 mm while keeping other parameters consistent. Uniform brazed joints were achieved at defocuses of +22, +25 and + 30 mm. Increased defocus distance resulted in a wider bead, with a larger laser irradiation area and expanded heat affected zone (HAZ), leading to increased steel melting and more Fe-rich precipitates within the Cu braze. The interfacial reaction layers remained Fe(Si) with increasing thickness as defocus changed from +22 to +30 mm, and two distinct Fe(Si) phases were initially identified. The steel Zn coating evaporated upon direct laser irradiation at upper two regions while participated in interfacial reactions at the weld root. At the weld root, a dramatic phase transition from Zn–Cu to Cu was observed, with liquid Zn(Cu) phases particularly forming in joints with a +30 mm defocus, led to solidification cracks that acted as failure initiation sites during tensile testing. Cracks propagated along the interfacial reaction layer/bead interface, or along large Fe-rich precipitates within the bead. A +30 mm defocus produced a lower hardness HAZ than with a +22 mm defocus, due to the higher content of bainite and tempered martensite resulting from a slower cooling rate. This work provides insights into optimizing laser brazing parameters for Zn-coated steel.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".