The Tightening and Untightening Modeling and Simulation of Bolted Joints
Why this work is in the frame
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Bibliographic record
Abstract
Although bolted joints may appear simple and are easy to manipulate, they are challenging to model and analyze due to their complex structural patterns and statically indeterminate nature. Ensuring the structural integrity of these joints requires maintaining proper bolt preload and clamping force, which is crucial for preventing failures such as overload, excessive bearing stress, fatigue, and stripping caused by seizing or galling. Achieving the necessary clamping force involves carefully controlling the input tightening torque, which is divided into the pitch torque and the friction torques at the bolt or nut bearing surfaces and in the engaged threads. The resulting clamping force is critical for generating the required force within the bolt. However, the achieved bolt force depends on several factors, such as friction at the joint’s contact surfaces, grip length, and the relative rotation between the bolt and nut during tightening. Friction at the contact surfaces, particularly beneath the bolt head or nut and between the threads, consumes a significant portion of the applied tightening torque—approximately 90%. This paper explores the three existing bolt internal pitch, bearing, and thread friction torques that are generated by the external applied torque in a bolted joint, as well as their contributions and variations throughout a loading cycle composed of three phases: tightening, settling, and untightening. An analytical model is developed to determine these torque components, and its results are compared with those obtained from finite element (FE) modeling and experimental testing from previous studies. Finally, this study examines the torque–tension relationship during bolt tightening, offering insights into the required accuracy of bolt and clamped member stiffness. The bolt samples used in this study include M12 × 1.75 and M36 × 4 hex bolts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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