Modified hybrid finite-discrete element modeling of compressive failure in alumina ceramics
Bibliographic record
Abstract
This paper presents a modified hybrid finite-discrete element model (HFDEM) for alumina ceramics, validated using quasi-static uniaxial compression experiments coupled with digital image correlation techniques. The model introduces a modified cohesive constitutive behavior with a general form of damage evolution law (including linear and power-law forms), adaptable to two types of alumina ceramics, to describe the processes of cracks growth from existing defects. Additionally, the model accounts for material flaw distribution by incorporating a microscopic stochastic fracture model. The modified HFDEM captures various phenomena involved in the compressive failure of advanced ceramics, including fracture growth following the axial loading direction, as well as catastrophic failure and fragmentation behavior. The proposed model was validated by comparing simulated quasi-static compressive stress–strain responses with experimental results. The model successfully reproduced two distinct fracture patterns observed in compression experiments, demonstrating its ability to accurately predict the mechanical response of alumina ceramics under uniaxial compressive loading. Once validated, the effects of some mechanical properties (e.g., Poisson’s ratio, elastic modulus, shear strength, and tensile strength) on the compressive stress–strain responses were explored. Notably, the compressive strength is primarily governed by the behavior of the crack elements in the model, which correspond to material flaws. The effect of increasing tensile strength on compressive strength becomes less significant. Conversely, shear strength significantly affects the peak compressive strength. Overall, this study provides a qualitative (e.g., fracture and fragmentation behavior) and quantitative (e.g., stress–strain response) understanding of alumina ceramic under quasi-static uniaxial compressive loading.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".