Determination of Fracture Modes in Novel Aluminum-Steel Dissimilar Resistance Spot Welds
Bibliographic record
Abstract
With increasing demands to improve vehicle fuel economy, multi-material body structures are increasingly utilized in the automotive industry for structural lightweighting purposes. These multi-material structures pose challenges in dissimilar material joining, particularly aluminum to steel. General Motors (GM) developed a new resistance spot welding technique using a multi-ring domed electrode and multiple solidification weld schedules to address these challenges. In aluminum-steel resistance spot welds (RSWs), an iron-aluminum intermetallic compound (IMC) layer is formed at the interface and its strength affects tensile shear specimen fracture modes, i.e. interfacial versus pull out fracture. Based upon the experimental heat affected zone (HAZ) and IMC shear strengths using a new mini-shear test specimen, it was observed that it was not suitable to use the critical weld nugget diameter of 4√t recommended by the American Welding Society (AWS) to determine the fracture modes of these unique aluminum-steel spot welds. In the present study, a new formula considering the shear strength of intermetallic layer in aluminum to steel RSWs is derived to calculate a critical aluminum-steel weld nugget diameter based upon experimental results. The calculated critical weld nugget diameters were then compared with experimental results to predict fracture modes for aluminum-steel stack-ups with different sheet thicknesses.
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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 | 0.001 | 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) | 0.001 | 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".