Progressive fatigue behavior of single-lap bolted laminates under different tightening torque magnitudes
Bibliographic record
Abstract
The present paper studies fatigue damage and life of single-lap bolted joints tightened with different torque magnitudes subjected to uniaxial load cycles. The adherends were constructed from E-glass/epoxy layers using a hand layup technique and assembled by 1.5, 3, and 8 N m of applied torques. Increasing the torque magnitude benefitted the final fatigue life of the joints so that the high-cycle fatigue life of the joint sample tightened with 8 N m was as high as 10 times that of the joint tightened with 1.5 N m. In the numerical section of this study, a three-dimensional finite element analysis was employed, and the impacts of applied torques were included in the progressive damage model to assess damage and failure in the bolted joints. For the joints tightened with higher torque levels, numerical results revealed higher fatigue lives but at the cost of more delamination at the vicinity of the hole. Laminate fracture surface was investigated through scanning electron microscopy and more cracking/damage progress was evidenced in matrix, fiber, and matrix–fiber interface as composite joints experienced fatigue cycles. Experimental life data of tested joints agreed with those anticipated through the use of finite element analyses indicating the developed model as an appropriate tool in evaluating the effects of applied torques on the fatigue fracture behavior of bolted laminates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".