The effects of Au and Ni plating on pulsed laser welding of thin sheets in the lap-joint configuration
Bibliographic record
Abstract
In electronic products and devices, base metals such as Al, brass, steels, Cu, Kovar and Ni are often plated with materials such as Ni, or Ni and then Au to enhance the corrosion and oxidation resistance or to improve the decorative appeal of the substrates. In this study, laser seam welds were performed on 200 μm thick sheets Al, Ni, Kovar and cold-rolled, plain carbon steel (CRS) in the lap-joint configuration using a Lumonics JK702H Nd:YAG pulsed laser welder. Uncoated, Ni and Au/Ni plated sheet was used. Tensile shear tests were performed and weld dimensions and weld microstructures were examined. Except for the Al specimens, the coatings did not affect the minimum beam intensity necessary for joining at the sheet interface. Once the weld pool penetrated across the interface into the second sheet, there was a very rapid increase in joint strength to a maximum strength value that was always less than the base metal strength. In almost all cases, failure occurred by shear in the heat affected zone (HAZ) at the fusion boundary. The Au/Ni plated Ni, Kovar and CRS specimens exhibited a Au/Ni braze at the sheet interface adjacent to the fusion boundary due to melting of the Au/Ni plating layers. In most cases, the shear strength of the joints was not affected by the plating; however, the presence of the Au/Ni braze caused a shift of the failure location away from the fusion boundary and into the HAZ or base metal.
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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".