An experimental-cohesive zone model approach to predict fatigue life of adhesive joints with varying modes of loading and joint configurations for automotive applications
Bibliographic record
Abstract
Predictive fatigue life models of adhesive joints are necessary to enable the assessment of automotive-bonded structures while reducing costly experimental testing. However, contemporary models have typically been calibrated for specific joint configurations and modes of loading, limiting their applicability to large-scale structures. Additionally, available models are based on simulation of cumulative fatigue cycling, making them computationally prohibitive. In the current study, cross-tension (CT) (load angles of 0°, 45°, and 90°) and single-lap shear joint (SLJ) configurations were bonded using an epoxy adhesive (BetaGuard CI6125R; PPG, France) used in automotive production (one part) and tested under fatigue cyclic loading. A total of nine joint configurations, having symmetrical (same material and thickness) and asymmetrical (dissimilar material or unequal thickness) joints, were tested. Fatigue tests at load levels between 25% and 75% of the static peak load were performed until joint failure or to runout (two million load cycles). The static tests of the joints were simulated to failure using finite element (FE) models with the cohesive zone method (CZM). The maximum strain energy release rates (Gmax) were calculated within the adhesive bond line at static loads corresponding to the peak loads of the fatigue tests. The Gmax values, computed from single cycle, specimen-specific FE simulations, were correlated with the measured fatigue life (Nf) of the adhesive joints with varying modes of loading and joint configurations. The fatigue life prediction model, based on Gmax−Nf correlation and following a crack propagation approach, predicted the cycles to failure for 85% of the fatigue tests, and 81% of the independent validation tests. The proposed fatigue life prediction approach provides computational efficiency and large-scale compatibility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".