Hybrid weld-bond joining technology of light metal alloys
Bibliographic record
Abstract
The biggest challenge faced by transportation industries in structural assembly is the joints’ integrity upon exposure to environmental conditions while maintaining excellent mechanical properties. Combination of mechanical and adhesive joining can provide high joint strengths, increased energy absorption and high fatigue lives. A weld-thru method has been previously employed where the parts to be assembled are friction stir welded (FSW) in the presence of a structural adhesive/sealant between the parts. However, this technology suffers adhesive damage due to weld-induced heat generated locally affecting the joints’ durability and mechanical properties. Also, most adhesive/sealants have Tg below the temperature attained during welding (450 to 500 oC), unable to withstand the weld-induced heat. In this paper, an inverse technology, called flow-in or weld-bond was used to bond Al-Al and Al-steel using a low viscosity epoxy adhesive in combination with FSW and arc welding processes. In this method, the welding step in overlap mode is performed first. The adhesive is applied later through the welded gap via capillary forces. Since welding and adhesive application are independently performed, the adhesive safely fills the gap while maintaining the joints’ integrity. The performance of the weld-bonded joints of pretreated Al and steel alloys as-assembled and after various environmental degradation (international standards) including combinations of heat, humidity, sub-zero temperatures, salt-spray and UV exposure, has been evaluated. The results show tremendous increase (1.7 times) in joint strengths on weld-bonded specimens in comparison to the weld-alone and adhesive-alone specimens with no change even after degradation. A simple surface treatment prior to adhesive application is found to help the joints sustain the salt-spray conditions. The fatigue life of the weld-bonded joints is found to be higher than those prepared using individual techniques.
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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) | 0.001 | 0.001 |
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".