Effect of Clamped Member Material and Thickness on Bolt Self-Loosening Under Transverse Loads
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bolted joints, prevalent in industrial applications for component fastening, are susceptible to self-loosening-a critical issue resulting in a gradual reduction in clamping force. Gaining insight into the underlying mechanisms of self-loosening is crucial. While prior research has largely focused on evaluating component stiffness, limited attention has been given to its impact on the self-loosening behavior of bolted joints under transverse cyclic loading. This study investigates how component stiffness influences self-loosening in bolted joints by varying the material and thickness of clamped members. An experimental setup replicating real-world conditions is devised to simulate loosening caused by cyclic lateral displacement. Tests are conducted using steel and high-density polyethylene (HDPE) clamped members of different grip lengths to explore the relationship between stiffness and self-loosening. Key parameters measured include bolt axial load, transverse force on clamped members, relative displacement, and rotation between the bolt and nut. The findings provide valuable insights into the effects of stiffness across various clamped member materials and grip length combinations, which can enhance the understanding of conditions that promote loosening resistance. Moreover, by highlighting stage-II or rotational loosening, with each test resulting in complete preload loss, the study provides a comparative analysis of the influencing factors. This enables the identification of distinct loosening patterns and supports the development of improved bolted joint designs to reduce loosening.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it