Induction Weld Seam Characterization of Continuously Roll Formed TRIP690 Tubes
Bibliographic record
Abstract
The weld seam characteristics of continuously roll formed and induction seam welded TRIP690 tubes were examined in this work. These tube are subsequently used in automotive hydroforming applications, where the weld seam characteristics are critical. The induction seam welds are created through a solid-state welding process and it was shown that by increasing the induction frequency by 26%, the weld seam width within the heat affected zone (HAZ) reduced due to a plateau in the hardness distribution which was a result of a delay in the transformation of martensite. 2D hardness distribution contours were also created to show that some of the weld conditions examined in this work resulted in a strong asymmetric hardness distribution throughout the weld, which may be undesirable from a performance perspective. An increase in the pressure roll force was also examined and revealed that a wider total weld seam width was produced likely due to an increase in temperature which resulted in more austenitization of the sheet edge prior to welding. The ring hoop tension test (RHTT) was applied to the tube sections created in this work. A Tensile and Notch style ring specimen were tested and revealed excellent performance for these welds due to high peak loads (~17.2 kN) for the Notch specimens (force deformation within weld) and lower peak loads (~15.2 kN) for the Tensile specimens for which fracture occurred in the base metal.
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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".