Analytical Modeling of Self-Loosening of Bolted Joints Subjected to Transverse Loading
Bibliographic record
Abstract
Self-loosening of bolted joints is a common issue in structural connections and machine design. 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 a new analytical model with an improved criterion that could be used to predict self-loosening of bolted joints. A complete study involving analytical, numerical, and experimental work has been conducted to give an insight of the mechanism leading to self-loosening of bolted joints and support the developed model. Particular focus is put on the effect of the clamping plate thickness on the self-loosening of the joint. A fully instrumented experimental setup, in conjunction with an M12-1.75 bolt, is deployed to track the bolt tension decrease with the application of an imposed cyclic transverse 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 finite element method (FEM) and experimentation for different plate thickness configurations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".