Strain rate-dependent behavior of cold-sprayed additively manufactured Al–Al<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg" display="inline" id="d1e499"><mml:msub><mml:mrow/><mml:mrow><mml:mi mathvariant="normal">2</mml:mi></mml:mrow></mml:msub></mml:math>O<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg" display="inline" id="d1e507"><mml:msub><mml:mrow/><mml:mrow><mml:mi mathvariant="normal">3</mml:mi></mml:mrow></mml:msub></mml:math> composites: Micromechanical modeling and experimentation
Bibliographic record
Abstract
Metal matrix composites (MMCs) fabricated by cold spray additive manufacturing (CSAM) are increasingly gaining attention as structural materials due to their rapid production and scalability. Herein, the failure behavior of CSAM Al-Al2O3 composites under quasi-static and dynamic compression was studied by an experimentally informed/validated 3D microstructure-based finite element (FE) model. The debonding mechanism was found to grow at a higher rate consequently dampening the particle cracking mechanism when the strain rate rises to dynamic regimes. The stress-bearing capacity of the particles plays a key role in enhancing the flow stress and elongation at failure of the CSAM composite under high strain rates due to the lower propensity of particle cracking. Eventually, the model was exercised to study the microscale failure progression in the material under elevated temperatures. For the first time in the literature, this study informs on the correlation between the microscale failure mechanisms and the mechanical performance of CSAM MMCs at the macro scale across strain rates and temperatures whose outcomes are applicable to the design of next-generation materials with a tailored performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".