Low-Temperature Deformation Mechanism and Strain-Hardening Behaviour of Laser Welded Dual-Phase Steels
Bibliographic record
Abstract
This paper analysed the change in microstructure after laser welding DP800 and DP1000, the effect of the laser welds on low temperatures deformation, and strain hardening behaviour when loaded at temperatures between −40 °C and 20 °C using quasi-static strain rates (1.7 × 10−2 s−1). The results showed that the fusion zone (FZ) was fully martensitic due to the rapid cooling during welding. Owing to the severity of the heat-affected zone, the joint efficiencies of DP800-DP800 and DP1000-DP1000 welds were 99.0% and 88.7%, respectively. The UTS, YS, and work hardening exponents of the welded joints increased slightly, while the strain hardening capacity of the base metals was much higher than those of the welded joints with decreasing temperatures. The evaluated work hardening exponents of the welded joints were determined using the Hollomon equation, Afrin equation, and Crussard-Jaoul analysis are in the range of 0.2–0.47, 0.24–0.59, and 0.45–0.71, respectively. The welded joints and the base metals demonstrated only stage III strain hardening, with DP800 joints exhibited excellent uniform and total elongation ranging between 8.0–8.7% and 10.4–14.2%, respectively. Fractures were located in the base metal of welded DP800 and SCHAZ of DP1000 welds, respectfully. The fracture surfaces demonstrated characteristic dimple fractures. The uniqueness of this study is found in its design, as there is currently no known literature on the low-temperature deformation mechanism and strain-hardening behaviour of similar DP800 and DP1000 welds.
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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".