A Simple and Practical Solution for Characterization of Adhesively Bonded Joints in Dissimilar Materials
Bibliographic record
Abstract
Fiber-reinforced polymer (FRP) composites are increasingly used in structural systems, replacing structural steel and aluminum. It is now well established that adhesive bonding is the most efficient mean of joining composites. Unfortunately, analytical models available in the literature offer design equations mainly applicable to balanced adhesive joints; where the two adherends are identical. In many practical applications, however, FRP composites are used (joined) in conjunction with other materials. This paper presents a simplified model that accurately predicts the behaviour of adhesive joints between different adherends. In this model, exponentially small terms are removed from the analytical solution, greatly simplifying the solution. The resulting design equations provide an accurate method of the design and analyzing of adhesive joints. The model applies to single-lap, single-strap and stiffener-plate joints, where shear and peel stresses are present. Furthermore, the model is easily extended to determine the energy release rate in adhesive joints. Results from the analytical model closely agree with finite element results, which are obtained in a fraction of the time and effort required for a non-linear finite element analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".