The Effect of Stud Manufacturing on Torque and Nut Factors
Bibliographic record
Abstract
Abstract Nut factors can vary depending on many factors, such as lubrication type, washers, stud material type, and thread dimensions. The industry has researched most of these factors extensively, but the variation of thread quality in stud manufacturing has been noticed but not researched. The authors of this paper have extensive experience using instrumented studs and have noticed that they have a different nut factor when using the same lubricant on flanges than on a Skidmore test fixture. In the past, the reasoning was a difference between a soft and a hard joint, but the hypothesis for this paper is that different stud manufacturers have different nut factors, depending on their manufacturing tolerances. They compared their results to data gathered on a Skidmore with non-instrumented studs. The authors asked if stud manufacturers have different manufacturing tolerances and if the stud manufacturing process should be as researched as lubrication. This paper tests three different stud manufacturers on a Skidmore and verifies their nut factor on flanges with ultrasonic bolt measurement. In total, over 1600 studs were tested. All testing was done on B7 studs with 2H nuts across five different stud sizes and flange configurations. The testing data shows a large variability in stud manufacturers, primarily the manufacturing lots.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".