Effect of Fiber Laser Welding on the Fatigue Properties of Dissimilar Welded Joints between DP980 and HSLA Steels
Bibliographic record
Abstract
Advanced high strength steels (AHSS) such as dual phase (DP) steels are now being extensively used to achieve light weighting goals of vehicles because of their attractive combination of formability and high strength. High strength low alloy (HSLA) steels are also used in lightweight bodies-in-white; DP and HSLA steels are therefore often laser butt-welded together into tailor welded blanks and to create other joints. Among the laser welding processes, fiber laser welding (FLW) has been shown to provide excellent quality welds, including superior weld mechanical properties, at higher speeds than those possible with other laser welding processes. Using dissimilarly welded DP980-HSLA blanks made with different welding parameters, the tensile properties were found to not change in spite of the HSLA being weaker than the soft zone on the DP980 side of the weld. The high heat input weld was found to have more softening in comparison to its base metal (BM) (55 HV versus 46 HV) and less bainite (8% versus 15%) in its FZ in comparison to the low heat input weld. The fatigue properties of the high heat input weld were also found to be better than those of the low heat input weld.
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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 | 0.001 |
| 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) | 0.002 | 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".