Effect of Multiple Pulse Resistance Spot Welding Schedules on Liquid Metal Embrittlement Severity
Bibliographic record
Abstract
Zinc-coated advanced high strength steels (AHSS) used in automotive applications are susceptible to liquid metal embrittlement (LME) during resistance spot welding (RSW). This study examines the impact of multiple pulse welding schedules on LME severity in welds of TRIP1100. Two different types of pulsing methodologies have been proposed to reduce LME severity: applying a pre-pulse before the welding current to remove the zinc coating and pulsing during the welding current to manage heat generation. However, the mechanisms by which these methods affect LME severity have not been fully explored. This work showed that a welding schedule consisting of two equal length pulses resulted in the least severe LME because it reduced the amount of free zinc available for LME without creating too much tensile stress. The majority of pre-pulse welding schedules caused an increase in LME cracking due to the additional heat introduced into the weld. However, a 4 kA (low current) pre-pulse applied for 3 cy (low time) reduced LME cracking by almost 30%. The pre-pulse allowed zinc to diffuse into the coating and stabilize the zinc, without introducing too much additional heat into the weld. These results indicate that multiple pulse welding schedules may be successfully used to reduce LME cracking, although the mechanisms by which they impact LME are more complicated than previously thought.
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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".