Mechanical Behavior of Adhesively Bonded Joints Under Tensile Loading: A Synthetic Review of Configurations, Modeling, and Design Considerations
Bibliographic record
Abstract
This review presents a comprehensive synthesis of recent advances in the tensile performance of adhesively bonded joints, focusing on applied aspects and modeling developments rather than providing a full theoretical analysis. Although many studies have addressed individual joint types or modeling techniques, an integrated review that compares joint configurations, modeling strategies, and performance optimization methods under tensile loading remains lacking. This work addresses that gap by examining the mechanical behavior of key joint types, namely, single-lap, single-strap, and double-strap joints, and highlighting their differences in stress distribution, failure mechanisms, and structural efficiency. Modeling and simulation approaches, including cohesive zone modeling, extended finite element methods, and virtual crack closure techniques, are assessed for their predictive accuracy and applicability to various joint geometries. This review also covers material and geometric enhancements, such as adherend tapering, fillets, notching, bi-adhesives, functionally graded bondlines, and nano-enhanced adhesives. These strategies are evaluated in terms of their ability to reduce stress concentrations and improve damage tolerance. Failure modes, adhesive and adherend defects, and delamination risks are also discussed. Finally, comparative insights into different joint configurations illustrate how geometry and adhesive selection influence strength, energy absorption, and weight efficiency. This review provides design-oriented guidance for optimizing bonded joints in aerospace, automotive, and structural engineering applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".