In-situ dual force: A novel pathway to improving the mechanical properties of resistance spot welds
Bibliographic record
Abstract
Although in-situ post-weld heat treatment (PWHT) has been a viable method to modify resistance spot weld microstructure and improve joint mechanical properties, prevailing methodologies only employ post-weld current to initiate microstructural transformations . This study uses in-situ dual force (DF) and a PWHT current pulse to induce microstructural changes specifically at the edge of the fusion zone (FZ), a region prone to crack propagation . After a short cooling period following the welding cycle, the application of strain energy from the DF and thermal energy from the PWHT current resulted in the formation of new equiaxed grains via austenite recrystallization. The energy absorption capability of the weld improved by 39 % after the DF schedule and 85 % when DF was combined with a PWHT current. The changes in mechanical properties resulted from strain hardening induced by the DF schedule, while grain refinement from the combined DF and PWHT current schedule led to the deviation of cracks at the edge of the FZ. In contrast, the crack propagated directly into the FZ along the columnar structure in the as-welded condition. The novel application of in-situ DF extends beyond the conventional PWHT and offers a promising avenue to trigger microstructural changes in the weld which can improve mechanical performance and overall crashworthiness.
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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 | 0.001 | 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".