Analysis of Industrial Quenching (Air Transfer + Oil Immersion) and the Cooling Regimes after Immersion
Bibliographic record
Abstract
Abstract Standard laboratory test methods are useful to compare the cooling performance and cooling regimes of different quenchants under controlled environments where quenching occurs almost immediately. In reality, many industries rely on systems that require transferring through air from the austenitizing furnace to the quench tank. In this project, a special quench probe apparatus is used to characterize an industrial quenching process involving air transfer followed by quenching in low viscosity oil. The probe system allows investigation of the non-homogeneous condition before immersion. The heterogeneity of the process, through air and in the oil, is captured by modifying the position and orientation of the quench probes among many experiments. Multiple characteristic points were identified during the boiling stage due to its physical significance to produce time dependent analytical curves built up through piecewise polynomial interpolation while an optimization algorithm models the convective stage. Inverse analysis is carried out with the data captured by the probes to estimate time dependent temperature boundary conditions. The output can further be computed into a temperature dependent heat transfer coefficient curve. Results indicate that the phenomena occurring after immersion differ from laboratory results thus demonstrating the significance of characterizing the actual industrial process.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".