Bibliographic record
Abstract
Twin-wire-arc spraying is an industrial process widely used to apply metallic coatings on surfaces of mechanical parts. Two consumable metal wires are continuously fed into the spray gun. An electric arc is struck between the tips of the two wires, melting them. A jet of high pressure gas blown over the tips of the wires removes the molten material, atomizes it into a spray of droplets and accelerates them towards a substrate where they land and freeze to form a protective coating layer. The physical processes controlling the twin-wire-arc system are complex because the turbulent fluid flow is supersonic, the arc plasma formed between the wire-tips is unsteady, and the wire material is constantly melted and atomized. Modelling the process is therefore difficult and requires extensive validation. The presented modelling technique, however, simplifies the problem by using pictures of the arc taken with different optical filters. The arc pictures are processed using image analysis software to determine the shape (i.e. curvature, length, and radius) of the arc. Arc equations are then solved based on the known shape/current of the arc, and heating due to the arc is evaluated and modelled as an external heat-source in the fluid flow model, including radiation effects. The presented model's prediction of the gas velocity is analyzed to evaluate shear stresses on the surface of wires and estimate the size of primary masses of molten material detaching from the wire-tips. The model predictions explain spatially-uneven particle-size-distribution in the twin-wire-arc plume and compare relatively well with experimentally measured particle sizes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".