Bulk nanostructure and amorphous metallic components using the electrospark welding process
Bibliographic record
Abstract
Abstract Purpose – The purpose of this paper is to outline the feasibility of using the electrospark welding (ESW) process to free‐form metallic components with nanostructured or amorphous microstructures. Design/methodology/approach – ESW was used to deposit amorphous and nanostructure coatings for high‐wear resistance applications. The ESW process was also used to freeform three‐dimensional objects via multiple deposition passes. The near‐net shape capability is interesting as it significantly reduces the post‐processing operations. Findings – This paper demonstrates that it is possible and economically feasible to produce components possessing metastable structures, i.e. nano or amorphous, using the ESW process. Practical implications – The ESW process possesses the ability to manufacture advanced materials and can pattern surfaces to provide appropriate functionality with respect to the service environment. Originality/value – This paper represents a summary of the capabilities of ESW to fabricate advanced materials and is based on the achievements of our laboratory. In particular, results on ESW of amorphous materials and the ability to produce coatings with second phase particles refined to this extent have not been achieved using other manufacturing methods.
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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".