Mitigating the influence of industrially relevant disturbances on LME severity of dissimilar resistance spot welded advanced high-strength steels
Bibliographic record
Abstract
The third generation of advanced high-strength steels (3G-AHSS) has been developed to provide high strength and high ductility, which attract automakers. To protect these materials from corrosion during service, these materials are typically coated with zinc. During resistance spot welding (RSW), the zinc coating can melt, allowing it to penetrate into the grain boundaries (GBs), and lead to liquid metal embrittlement (LME) phenomena. Concerns regarding LME susceptibility have impacted the industrial application of 3G-AHSS; therefore, its mitigation has become a top focus for automakers. Several possible strategies for lowering LME severity by altering welding parameters have been proposed to mitigate LME in similar spot weld joints. However, these strategies were not tested on a dissimilar spot weld joint. Therefore, in this work, 1.4 mm gauge thickness galvanized (GI-coated) 3G-980 AHSS was joined with 0.6 mm thick Interstitial Free (IF) steel. In this work, current pulsation and ultra-short hold time were proposed to minimize LME severity. The robustness of the developed welding schedule was then tested on welds made with industrial disturbance factors such as pre-strained sheets (between 0 to 80% of 3G-980 material yield strength) and electrode misalignment (between 0° to 10° misalignment) compared to baseline parameters. In severe circumstances of disturbance factors, the resulting optimized welding schedule decreased LME cracking and showed improved resistance to LME, lowering LME severity by 41% for the extreme pre-strain condition and 27% for the extreme misalignment angle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".