High-deposition laser brazing with enhanced surface quality, bridge performance and mechanical properties
Bibliographic record
Abstract
Laser brazing of Zn-coated steel in a flare V-groove configuration was conducted using high wire feed rates (WFR) at travel speeds (TS) of 2.5 m/min. With a high WFR/TS ratio of 1.6–2.2, high-quality joint surface without visible surface defects was obtained exhibiting surface roughness values from 1.6 to 2.2 μm, and the bridge width and height were improved. The fracture load and nominal tensile strength were improved with wire deposition rate, reaching maximum values of 3.94 kN in a 15-mm long weld, or a nominal equivalent tensile stress of 529 MPa at high WFR/TS ratios of 2 and 1.8 respectively, due to an enhanced bead strength resulting from decreased content of Fe mixed in the bead. This suppressed formation of Fe-rich precipitates, while refined the solidified bead microstructure. With the enhancement of the bead strength, the interfacial reaction layer/bead interface becomes a weak-bonded interface due to a mismatch in the lattice structure and mechanical properties in the braze versus base metal, with fracture initiating from the unwetted gap at the steel/bead interface. With low WFR values of 3 and 3.5 m/min, the joint easily fractured across the bead due to segregation of large Fe-rich precipitates and hot cracking within a concave joint geometry. The joint produced with a WFR of 4 m/min fractured by a combination of through-bead and interfacial fracture. This work provides an insight for improving the mechanical properties and broadening the application of laser brazing. • High-deposition laser brazing of GA-coated DP600 with a high WFR/TS ratio was investigated. • Joints showed surface roughness of 1.6–2.2 μm with enhanced surface quality and bridge property. • The fracture load was improved by 74 % with WFR, reaching a maximum value of 3.94 kN. • High wire deposition reduces and refines Fe precipitates, enhancing the strength of the braze bead. • Joint with low WFR easily fractured across the bead due to severe segregation of Fe precipitates and hot cracking.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".