Parametric Study of Automated Electrospark Deposition for Ni-Based Superalloys
Bibliographic record
Abstract
Conventional electrospark deposition (ESD) processes used in industry are well suited to the coating and repair of small areas for the purpose of hardfacing, corrosion resistance, or dimensional restoration. Although significant advances have been made in the range of materials that can be processed, the comparatively slow deposition rate limits the potential applications of a traditional manually operated ESD process. In this study, an automated ESD system was demonstrated for the application of Ni-based superalloy (Inconel 718) coatings on Ni- and Fe-based substrates. A preliminary study was used to determine the influence of process parameters on an automated system, with capacitance, voltage, electrode force, and electrode travel speed parameters chosen to provide higher deposition rates while maintaining high deposition quality. A comparison of Inconel 718 and 316L stainless steel substrates found that the influence of substrate composition on coating hardness and coating composition was limited to the first 40um. These results pave the way for ESD of larger-area coatings and longer-duration repairs without the need for human operators.
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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 | 0.001 |
| 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 | 0.000 |
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".