Coatings on Resistance Welding Electrodes to Extend Life
Bibliographic record
Abstract
TiCP/Ni coating has been deposited onto the electrodes by electro-spark deposition to improve electrode life during resistance welding of Zn-coated steels. However, welding results revealed that molten Zn penetrates into coating through the cracks and then reacts with substrate copper alloy to form brasses. In the present work, laser treatment was performed on the TiCP/Ni coated electrodes to eliminate cracks formed in the as-deposited TiCP/Ni coating. In addition, a multi-electro-spark deposition of Ni, TiCP/Ni and Ni has also been carried out to improve coating quality. On the other hand, a TiB2 coating was also investigated. those coatings were characterized by electro-microscopy, energy-dispersive X-ray analysis, X-ray diffraction and micro-hardness tests. The results showed that cracks within the as-deposited TiCP/Ni coating could be eliminated with the use of laser treatment or a multi-layer deposition process. However, softening of copper substrate after laser treatment was a problem that restricted welding performance. Welding tests showed that the multi-layer Ni/(TiCP/Ni)/Ni coating acted as a better barrier to decrease alloying between the copper alloy and molten Zn as well as pitting (erosion) of electrode. The TiB2 coating demonstrates its potential application on the electrode to further improve the electrode's life.
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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) | 0.003 | 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".