A Phenomenological Model of Resistance Spot Welding on Liquid Metal Embrittlement Severity Using Dynamic Resistance Measurement
Bibliographic record
Abstract
Abstract Liquid metal embrittlement (LME) is one of the severe problems of Zn-coated steel in resistance spot welding (RSW). Hence, proper welding schedules for Zn-coated steel are of practical interest. RSW involves a complex interaction between electrical, thermal, and mechanical phenomena. Identification and integration of all these governing physics are almost impossible by performing simple experiments. Hence, phenomenological modeling of RSW has gained a significant attention in the recent past. The complexity of the physical process introduced by the dynamic nature of contact resistance brings challenges for the model. A simplified but effective modeling approach of RSW is proposed where attention is focused on the evaluation of the thermal field using the finite element (FE) method. The interaction of the mechanical and electrical field is performed by the dynamic variation of the contact area, stress concentration, and non-uniform current density distribution in a semi-analytical model. These internal variables of the model are incorporated through the scaling of the governing parameters by the dependence of the transient and converged temperature field within a time step. The transient-dynamic contact resistance is detached from the measurement of total resistance and mapped adaptively by the implicit scheme within a time step of the numerical model. The transient development of the nugget is investigated for dual-phase steel (DP980) with an interrupted test of the dynamic resistance curve. The FE model is validated with experimentally measured results at different process conditions. The characterization of the thermal history from the model relatively identifies the LME phenomena and suggests corresponding modification of the welding schedule.
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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 | 0.001 | 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) | 0.000 | 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".