Impact of orientation and stacking on knife distortion during quenching
Bibliographic record
Abstract
The impact of stacking and knife orientation on distortion in vacuum furnace heat treatment was investigated using a sample of 900 knives made from AISI mod-A8 tool steel. The knives were distributed across 5 levels, with each level containing two tiers. Each tier held 45 knives, divided into 15 lots of 3 stacked knives each. Results revealed significant distortion in knives positioned at the bottom of the furnace, specifically in the first level (tiers 1 and 2). Notably, the knives in the middle of each stack consistently exhibited higher levels of distortion compared to those at the top or bottom of the stack. This pattern suggests that the position within both the furnace and the individual stack plays a crucial role in the degree of distortion experienced by the knives during the heat treatment process. These findings have important implications for optimizing the heat treatment process in vacuum furnaces, particularly for knife manufacturers.
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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 | 0.001 | 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".