Strength Optimisation of Hybrid Bolted/Bonded Composite Joints Based on Finite Element Analysis
Bibliographic record
Abstract
A finite element analysis (FEA) was conducted to examine the behaviour of single-lap quasi-isotropic (QI) and cross-ply (CP) hybrid bolted/bonded (HBB) configurations subjected to tensile shear loading. Several critical design factors influencing the composite joint strength, failure conditions, and load-sharing mechanisms that would optimise the joining performance were assessed. The study of the stress concentration around the holes and along the adhesive layer highlights the fact that the HBB joints benefit from significantly lower stresses compared to only bolted joints, especially for CP configurations. The simulation results confirmed the redundancy of the middle bolt in a three-bolt HBB joint. The stiffness and plastic behaviour of the adhesive were found to be important factors that define the transition of the behaviour of the joint from a bolted type, where load sharing is predominant, to a bonded joint. The load-sharing potential, known as an indicator of the joint's performance, is improved by reducing the overlap length, using a low-stiffness, high-plasticity adhesive, and using thicker laminates in the QI layup configuration. Enhancing both the ratio of the edge distance to the hole diameter and washer size proves advantageous in reducing stresses within the adhesive layer, thereby improving 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".