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Record W4389540773 · doi:10.17118/11143/21079

Stress state and strain-rate dependent failure of additively manufacturedceramics : overview, experiments and modeling

2023· article· en· W4389540773 on OpenAlexaffabout
Mohammad Rezasefat, Zahra Zaiemyekeh, Haoyang Li, James D. Hogan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCeramicStress (linguistics)Materials scienceStrain (injury)Stress–strain curveState (computer science)Composite materialComputer scienceDeformation (meteorology)Algorithm

Abstract

fetched live from OpenAlex

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.,

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.253
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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