Fatigue of Cold Spray-Assisted Resistance Spot Welding of Aluminum to Magnesium
Bibliographic record
Abstract
Direct fusion welding of Al to Mg creates brittle intermetallics (IMCs) that restrict the application of these joints in structural applications. An interlayer foil can be employed to prevent intermetallic formation allowing successful resistance spot weld joint. However, the presence of a foil increases the part count, and is difficult to place during welding. Cold spray coating is a promising solid-state coating deposition technology used here to provide an enhanced interface to facilitate joining Al to Mg sheets by means of resistance spot welding. The ability of cold spray coating technology to deposit an intermediate layer on a substrate surface using powder material is shown to be beneficial in restricting the formation of the brittle intermetallic between Al and Mg. In this study, Ni powder has been successfully coated onto Al and Mg sheets. The Ni-coated coupons were then welded via resistance spot welding using optimized welding parameters. Successful welds were produced using 27 kA for 15 cycles in 2 pulses with 5 delay cycles between pulses. Metallurgical bonding between the Al, Mg, and Ni coating in the fusion zone were revealed by using scanning electron microscopy. It is shown that the bonding between the three elements prevents the formation of Al-Mg IMCs and restricts formation of deleterious IMCs. Fatigue test was then performed to evaluate the joint performance under cyclic load. The develop weld survived 2M cycles at maximum cyclic load of 1.28 kN at R=0.2. While there are no reports on fatigue of direct weld of Al/Mg, the monotonic tensile strength of lap shear samples of the developed weld is five-fold more than the direct Al/Mg welds, showing significant improvement.
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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 | 0.000 | 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".