How Accurate Is Torque? Improving the Accuracy of Torque by Leveraging Stud Manufacturers, Tooling, and Practices
Bibliographic record
Abstract
Abstract This paper investigates strategies for improving torque accuracy in bolted flange assemblies by evaluating the influence of stud manufacturers, tooling types, and bolting patterns. Torque application is the most common method for achieving axial load in bolted joints. Still, traditional approaches have exhibited a +/−30% accuracy, largely due to variations in friction and assembly practices. To address these concerns, the authors conducted a series of tests using studs from three different manufacturers, applied with three types of torque tools (hydraulic, battery-powered, and manual wrenches) across three bolting patterns: the star, modified star, and quadrant. Nut factors were determined using Skidmore-Wilhelm test fixtures, and ultrasonic (UT) measurements were employed to assess actual bolt stress during flange assembly. The results of over 1600 studs tested show that stud quality is critical in achieving more consistent torque accuracy, with significant variations in nut factors observed between manufacturers. Among the tooling tested, hydraulic wrenches provided the highest accuracy, followed by manual tools, while battery-powered tools excelled in speed. Bolting patterns did not show significant differences in load distribution, but the quadrant pattern offered advantages in assembly time. These findings emphasize the importance of high-quality fasteners and appropriate tooling in reducing torque variance, ultimately enhancing the reliability of flange joints.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".