Experimental and Numerical Study on Bolted/Bonded Composite Joints for Aircraft
Bibliographic record
Abstract
Bolted and bonded joining technologies in composite structures have been widely studied since the 1970s. Although every technology has advantages and disadvantages, bonded joint technology has the potential to reduce the weight and manufacturing cost of advanced aircraft structures. However, bolted joining is still generally required for certification and regulatory compliance. An alternative to these technologies is the use of hybrid bolted/bonded joints combining elements from both technologies. When compared with bolted and bonded technology in composite structures, hybrid joining technology is at an early stage of development, very few journal papers have been written on this topic as of the time of this writing. Previously reported research efforts have been focused on the load sharing and strength improvement. The load sharing has been analyzed experimentally using instrumented bolts and numerically using finite element modelling. The strength analysis has been studied experimentally. The present research was proposed to study hybrid bolted/bonded joints experimentally using a “design of experiments” approach. The aim was to investigate the effect of several factors on the joint strength and load sharing in bolted/bonded hybrid joints. In addition to this, finite element modelling was successfully applied to predict the load sharing and strength, and the results were compared with the experiments with good agreement. The instrumented bolt technique is limited due to the bolt size. To surpass this limitation with the proposed design of experiments, a different approach was needed. Along with the design of experiments and numerical analysis, a novel technique using digital image correlation to measure the shear strains at the adhesive edge was applied to measure the load sharing. Also, the load sharing was computed in all the joint configurations, not only a single sample joint configuration, as was typically reported in other studies. The numerical results in terms load sharing showed a very good agreement with the experiments. On the other hand, using cohesive zone modelling, the strength was predicted with a good agreement compared with the experimental results. Finally, the analysis of variance from the design of experiments quantified the effect of the proposed factors in the joint strength.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".