Analytical Modeling of Self-Loosening of Bolted Joints
Bibliographic record
Abstract
Self-loosening of bolted joints is a common issue in Pressure Vessel and Piping equipment. This phenomenon can not only cause the industry a lot of losses due to maintenance but can result in catastrophic incidents leading to environment and health issues. This paper presents several improvements and innovations that are brought to the subject of self-loosening of bolted joints through a study involving analytical, numerical and experimental work. A new model to prevent self-loosening of bolted joints is developed. Particular focus is put on the effect of the clamping plate thickness on the self-loosening of the joint. A fully instrumented experimental set-up, in conjunction with an M12-1.75 bolt, is deployed to track the bolt tension decrease with the application of an imposed cyclic transversal displacement. In addition to the measurement of the transverse relative displacement between the two clamping parts, the clamping and transverse loads are also recorded. Finally, the relative rotation between the bolt and the nut is measured by means of a rotation sensor directly installed on the nut. The results show that there is a good agreement between the analytical, numerical and experimental results. The new model is compared with FEM and experimentation for different plate thickness configurations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".