Mechanics of hybrid bonded-bolted joints
Bibliographic record
Abstract
Large mechanical structures, for example aircraft and motor vehicles, usually consist of a number of subassemblies.This necessitates the use of joints, which locate the subassemblies and transfer loads between them.While necessary, joints introduce discontinuities into the structure that act as stress raisers and regions of possible failure initiation.Consequently, joint design is crucial for achieving satisfactory structural strength.Joining composite structures is all the more challenging compared to metallic structures.This is due to the brittle nature of many composite materials, which permits only modest plastic deformation and limits the corresponding mitigation of stress concentrations prior to fracture.This is particularly problematic for bolted composite structures.In order to fully exploit the potential of composites, it is thus necessary to develop more efficient means of joining them.In recent years, several investigators have shown experimentally that a combination of bonding and bolting can, sometimes, produce a joint that is stronger than either of these joints by itself.This could potentially result in a more efficient joint and is therefore of particular interest for composite structures.These investigators found that load sharing between the adhesive and bolt is important for achieving the benefits of "hybridization".However, they had only limited understanding of the most effective way to achieve load sharing.Furthermore, they made no attempt to predict the strength of hybrid joints by means of mathematical modelling.In response to these shortcomings in the literature, this thesis follows a twopronged approach.First, in Chapters 3-4, load sharing in hybrid bonded-bolted joints is addressed.An efficient numerical model is developed in Chapter 3 for this end.This model is validated experimentally, after which it is used in a global sensitivity analysis in Chapter 4 to determine the most important factors influencing load sharing.Of the various parameters considered, it is found that the adhesive yield strength, overlap length and adhesive hardening modulus have the greatest effects, by a considerable margin.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".