Stress state and strain-rate dependent failure of additively manufacturedceramics : overview, experiments and modeling
Bibliographic record
Abstract
Additive manufacturing of ceramics offers several advantages over traditional manufacturing methods which include design flexibility, reduced material waste, reduced manufacturing complexity, and the possibility for faster prototyping and production. Despite the growing interests in additively manufactured ceramics (AMCs), their stress state and strain-rate dependent behavior is still not fully understood. This is a significant challenge, as the use of AMCs under complex loading and high strain rate conditions requires a detailed understanding of the underlying mechanisms governing their mechanical behavior and fracture. This study presents a comprehensive look at AMCs focusing on their behaviour under complex loading and high strain rate conditions taking into account: (i) the additive manufacturing techniques and related parameters such as applicability to different ceramic materials, temperature, pressure, sintering, surface finishing, and potential for defects, (ii) experimental characterization using different specimen geometries such as compression, shear compression, indirect tension, and semi-circular under quasi static and dynamic (using a split-Hopkinson Pressure Bar) loading, (iii) evaluation and benchmarking versus ceramics made by traditional methods which includes characterization at microstructure level using EBSD, SEM, and TEM, and comparison of macroscale mechanical properties, and (iv) development of multi-scale numerical models incorporating relevant physics and microstructural features, informed by the experiments at each scale. The simulations are aimed at integrating the information obtained from lower scales (mesocale simulations at grain level and molecular dynamic simulations) to develop more accurate and informed macroscale numerical frameworks. Models also allow for the evaluation of additive manufacturing techniques by considering material orientation, grain size, and porosity on the behavior of the printed ceramic at different scales. Overall, predictive modelling tools informed by testing and characterizing today can guide design of weight-optimized high-performance additively manufactured ceramic-based structures for use in vital Canadian industries (e.g.,
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".