Testing the Accuracy and Repeatability of Common Torquing Equipment
Bibliographic record
Abstract
Abstract Proper torque application by tools is critical for achieving target axial loads in bolted flange joint applications. This study builds on previously published papers by investigating the accuracy and repeatability of three types of torque wrenches (manual click-type, hydraulic low-profile, and battery-powered pistol grip) commonly used in bolted flange joint assemblies. The authors conducted comprehensive testing on over 400 studs across five distinct flange configurations, utilizing Ultrasonic Bolt Measurement for precise evaluations. Our findings reveal that hydraulic torque wrenches exhibit the highest accuracy, consistently achieving target torque values within ±3%, followed closely by manual torque wrenches, which maintained an accuracy within ±5%. In contrast, battery-powered wrenches displayed higher variability, with inaccuracies averaging ±5.5%. The study also highlights the significant role of operator skill in the performance of manual tools, suggesting that effective training is essential for maximizing accuracy. While hydraulic wrenches proved superior in repeatability, the faster torque application of battery-powered tools led to greater scatter in results. Overall, this research underscores the importance of proper tool verification and selection in achieving reliable bolted joint assembly outcomes. It demonstrates that tooling can contribute to an accuracy variance of up to ±38% under field conditions. The data presented offers valuable insights for industry practitioners in choosing effective torque application methods.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".