On the Use of Shape Memory Alloy Studs to Recover Load Loss in Bolted Joints
Bibliographic record
Abstract
Creep is an important factor that contributes to the clamp load loss and tightness failure of bolted joints with and without gaskets over time. Retightening of the joint can be expensive and time consuming; therefore, it is an undesirable solution. Currently, most efforts are put towards reducing load losses directly by tightening to yield, improving material creep properties, or making joint less rigid. An alternative solution of current interest is the use of bolts in shape memory alloys (SMAs). However, very few experimental studies are available, which demonstrate the feasibility of these alloys. The objective of this study is to explore the benefit of shape memory and superelasticity behavior of an SMA stud to recover load losses due to creep and thermal exposure of a gasket in a bolted-joint assembly. This paper explores several venues to investigate and model the thermomechanical behavior of a bolted joint with a nickel–titanium SMA stud. A stiffness-based analytical model which incorporates the Likhachev model of SMA is used as a representation of an experimental bolted-joint assembly. Based on this model, the rigidity of the experimental setup is optimized to make the best use of the SMA properties of the stud. This analytical model is compared with a finite element model, which also implements the Likhachev's material law. Finally, an experimental test bench with a relatively low stiffness representative of standard flanges is used, with and without gaskets to demonstrate the ability of the SMA stud to recover load losses due to gasket creep.
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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 | 0.000 |
| 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 | 0.001 | 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".