Characteristics of a bolted joint with a shape memory alloy stud
Bibliographic record
Abstract
Creep is an important factor that contributes to the load loss and tightness failure of bolted joints.Retightening of the joint can be expensive, time consuming and therefore is an undesirable solution.Currently most efforts are focussed on reducing load losses directly by tightening to yield, improving material creep properties or making joints less rigid.An alternative solution of current interest is the use of bolts in shape memory alloy (SMAs).However, very few experimental studies are available that demonstrate its feasibility.The objective of this study is to exploit the benefit of the shape memory and superelasticity behaviors of a SMA stud to recover the load losses due to creep and thermal exposure of a gasket in a bolted joint assembly.This paper explores several avenues to investigate and model the thermo-mechanical properties 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.Using this model the rigidity of the experimental setup is optimized to make the best use of the SMA properties of the stud.This theoretical model is validated by a Finite Element (FE) Model using a custom FE material model which also implements the SMA material model.Finally an experimental test bench with an optimized stiffness derived from analytical simulations is used, with and without gaskets to demonstrate the ability of the SMA stud to recover load losses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".