The tightening and untightening of bolted joints
Bibliographic record
Abstract
Although very simple in appearance and manipulation, a bolted joint is a complex structure that is difficult to analyze because it is a statically indeterminate problem. To ensure the structural integrity, the tightening of a bolted joint is an important operation worth looking at in order to achieve a consistent bolt preload and clamping force without seizing or galling the bolt. The required bolt preload can be achieved by controlling the tightening torque that is distributed into the pitch torque and the friction torques developed in the threads and under the bolt and the nut if used. However, for a particular bolt size and value of the tightening torque, the bolt preload can vary due to several factors. The friction present in the different contact surfaces of the bolt, the gage length and the nut or bolt turning are to name a few. Notably, 90% of the applied torque required to tighten the bolt is to overcome the friction in the contact surfaces between the threads and under the head or nut. This paper aims at studying the contribution of the various types of torques (pitch, bearing and thread friction torques) and their variation during tightening, untightening and at rest in a bolted joint. An accurate model is developed to evaluate these different torques and compared them to those obtained from FEM simulations and experimentally from other research. The study also looks at the torque-rotation relationship during the tightening of a bolt as to observe the role of the clamped members and bolt stiffness. The study will be conducted on an M12 x 1.75 hex bolt.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".