On the Measurability and Predictability of HAZ Softening in GMAW of Automotive DP980 Steel
Bibliographic record
Abstract
Dual Phase (DP) steel, composed of a ferrite matrix with dispersed islands of martensite, has become popular in auto-body car construction due to its outstanding mechanical properties (i.e., high strength and good ductility). DP steel softens at the sub-critical heat-affected zone (SC-HAZ) when subjected to welding thermal cycles, owing to the tempering of the martensite phase. In this work, DP980 steel was subjected to varied thermal cycles: (a) furnace-tempering treatment, (b) gas metal arc welding (GMAW), and (c) resistance spot welding (RSW), in order to characterize the tempering of martensite below the Ac1 critical temperature and at the sub-critical heat-affected zone (SC-HAZ) in the case of the welded specimens. The coarsening stage of cementite phase was characterized through microstructure observations and hardness measurements. As expected, the comparative results indicated an advanced stage of the martensite tempering in the furnace heat-treated specimens, followed by the GMAW and the RSW specimens. Further, developed softening kinetic models have been suitably employed and adjusted in order to predict the extent of softening along the SC-HAZ of the GMAW specimen. Finally, as the advanced stage of cementite coarsening is due to the influence of the arc welded thermal cycle, a reasonable estimation of the hardness profile was obtained, particularly for tempering temperatures above 400 °C.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".