3D Microstructure-Based FE Simulation of Cold-Sprayed Al-Al2O3 Composite Coatings under Indentation and Quasi-Static Compression
Bibliographic record
Abstract
Abstract This study developed microstructure-based finite element (FE) models to investigate the behavior of cold-sprayed aluminum-alumina (Al-Al2O3) metal matrix composite (MMCs) coatings subject to indentation and quasi-static compression. Based on microstructural features (i.e., particle weight fraction, particle size, and porosity) of the MMC coatings, representative volume elements (RVEs) were generated by using Digimat software and then imported into ABAQUS/Explicit. State-of-the-art physics-based modelling approaches were incorporated into the model to account for particle cracking, interface debonding, and ductile failure of the matrix. This allowed for analysis and informing on the deformation and failure responses. The model was validated with experimental results for cold-sprayed Al-18 wt.% Al2O3, Al-34 wt.% Al2O3, and Al-46 wt.% Al2O3 metal matrix composite coatings under quasi-static compression by comparing the stress versus strain histories and observed failure mechanisms (e.g., matrix ductile failure). The results showed that the computational framework is able to capture the response of this cold-sprayed material system under compression and indentation, both qualitatively and quantitatively. The outcomes of this work have implications for extending the model to materials design and under different types of loading (e.g., erosion and fatigue).
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".