Multiscale numerical modeling and experimental validation of additivelymanufactured alumina ceramics
Bibliographic record
Abstract
Additively manufactured ceramics (AMC) have gained popularity due to their flexibility in the design and fabrication of industrial complex geometries with multi-functionalities when compared with traditionally fabricated ceramics. In this study, we investigate the behavior of alumina ceramics additively manufactured using stereolithography (SL) through multi-scale computational and experimental mechanics approaches. The SL method is based on the deposition of consecutive layers of a photoreactive material, where the laser energy and exposure time of each layer was set to be 30 mW/cm2, and 10 s, respectively. All specimens were sintered at 1650C for 2 hours after debinding. The material microstructure was characterized using EBSD, SEM, and TEM to provide inputs for multiscale modeling. The AMCs with two different printing orientations (POs) were tested under dynamic strain rates by using a split-Hopkinson Pressure Bar setup coupled with ultra-high-speed imaging and digital image correlation analysis. Next, 3D polycrystalline RVEs were generated using Neper software, and these were informed by the experimentally measured grain size distribution and porosity. To model the transgranular fracture, a viscosity regularized plasticity Johnson-Holmquist model (i.e., JH2-V model) was implemented by using a VUMAT subroutine in the Abaqus. The intergranular fracture mode was accounted for by implementing a cohesive zone model at the grain boundaries. The micromechanical model was quantitatively (i.e., stress-strain histories) and qualitatively (i.e., intergranular, and transgranular failure mechanisms) validated by experimental data. Our experimental data showed the strength of the present AMCs is lower than that of the conventionally made ceramics by ~ 40% and ~ 25% under quasi-static and dynamic rates, respectively. It was also revealed that the failure pattern in the AMCs is affected by the POs. The present microstructure-informed model provided a better understanding of the initiation/competition of failure mechanisms of the AMCs that are challenging to unravel through purely experimental approaches. Altogether, the present multiscale modeling framework allows for correlating the microstructural characteristics of the AMCs, and interface properties to the macroscale response of the material (e.g., strength) to provide microstructure-propertyperformance relationships, and this has implications for designing future weight-optimized additively manufactured ceramicbased structures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".