Design Concepts for Peel-Dominant Adhesive Joints in Aeronautic Applications
Bibliographic record
Abstract
The adhesive bonding technique is employed from the aeronautical/aerospace industry to current house products. To comply with the requirements of distinct applications, different joint configurations are available to the designer. While single-lap joints (SLJ) are the most common in application and research, double-lap joints, scarf joints and T-joints find specific applications. T-joints are seldom studied in the literature, but these are used, for instance, in aircraft to bond the stiffener beams to the skin, or in the cars between the B-pillar and the rocker. Due to the high stress concentrations, T-joints often fail under average stresses much lower than the adhesive strengths, giving rise to the necessity for proper design and strength improvement methodologies. This work initially aims to validate the cohesive zone modelling (CZM) technique with experiments, and then use it to numerically evaluate and optimize the performance of T-joints subjected to peel loads. CZM is nowadays regarded as the most powerful strength prediction tool for adhesive joints, and can be a valuable tool to improve T-joints. Different features are addressed for a complete analysis: adhesive type, geometrical parameters, dual-adhesive technique for strength improvement, and composite joints. The evaluated geometrical parameters are the base adherend thickness (a), T-part thickness (t), overlap or bonding length (l) and curvature radius (r). As a result of this work, the model was successfully validated, and clear design guidelines were provided to define the ideal geometric and material (adhesive) conditions for best performance.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".