A review on the laser welding of coated 22MnB5 press-hardened steel and its impact on the production of tailor-welded blanks
Bibliographic record
Abstract
The current demand for vehicles with high fuel efficiency, improved safety and enhanced crashworthiness qualities is being met by making use of high strength components with tailored mechanical properties which are made using tailor-welded blanks. During their production, the surface of the blank needs to be protected against oxidation and decarburisation. Protective coatings are used to protect the steel surface, with Aluminium-Silicon or Zinc-based coatings being the most popular. This work provides a review on the state-of-the-art as well as the issues associated with the laser welding of coated 22MnB5 grade steel to be used in the production of tailor-welded blanks. The paper provides a summary of existing solutions available to overcome these issues while discussing their limitations and the potential for future work.List of Abbreviations and Symbols: AHSS: Advanced-high Strength Steel; UHSS: Ultra-high Strength Steel; UTS: Ultimate Tensile Strength; PHS: Press-hardened Steel; HS: Hot Stamping; AR: As received; LWB: Laser-welded Blank; TWB: Tailor-welded Blank; ARW: As-received Welded; ARWHS: As-received Welded then Hot-Stamped; HSW: Hot-Stamped then Welded; BM: Base Material; HSBM: Hot-Stamped Base Material; BOP: Bead-on-Plate; CR: Cooling Rate; DIC: Digital Image Correlation; DP: Dual Phase; EPMA: Electron Probe Micro-Analyzer; EDX: Energy Dispersive X-Ray Spectroscopy; FLW: Fibre Laser Welding; FZ: Fusion Zone; GI: Galvanised; GA: Galvannealed; HAZ: Heat-affected Zone; LBW: Laser Beam Welding; LME: Liquid Metal Embrittlement; PAG: Prior Austenite Grains; BIW: Body-in-white
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".