Bibliographic record
Abstract
Abstract A substrate surface thermocouple was developed for thermal spraying. The substrate used for the study is a porous 430 stainless steel disk, though the thermocouple concept can be applied with other materials. Type N thermocouple wires are cemented in holes through the substrate, and then a copper coating is deposited across the surface to electrically connect the wire tips to complete the thermocouple circuit. The copper also promotes temperature equalization between the wire tips and the surrounding substrate surface to increase accuracy. Using finite element analysis (FEA), it was determined that the optimum thickness of the copper layer is 38 µm. With this thickness, the thermocouple should be able to measure peak-to-peak surface temperature swings due to a passing plasma jet within +/-3% when the copper thickness is uniform and all physical properties of the coating and substrate system are well-known. However, a number of assumptions were used for the FEA, so a detailed uncertainty analysis was performed. This analysis found that the expected accuracy window of the thermocouple is +19%/-10% as implemented for measuring surface temperature swings. For measuring average temperatures, the thermocouple is very accurate, because large heat fluxes into the substrate occur only when the plasma torch is directly in front of the substrate. Experimental measurements of surface temperatures with the optimized thermocouple are presented.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".