3D microstructure-informed modeling of cold-sprayed additivelymanufactured Al-Al2O3 composites : computational damage analysis
Bibliographic record
Abstract
The failure behavior of Al-Al2O3 composites additively manufactured by cold spray technology is computationally investigated using 3D microstructure-based physically-informed finite element (FE) models. To inform the model, the microstructural features, including porosity, particle size and content were captured by scanning electron microscope (SEM) images. Representative volume elements (RVEs) were generated by using Digimat software based on microstructural inputs. For the Al metal matrix and the Al2O3 reinforcing particles, stress-state-dependent constitutive material models were incorporated into the RVEs by using VUMAT subroutines in Abaqus/Explicit FE solver. The micromechanical model was validated by the experimental stress-strain curves and the failure mechanisms observed in the SEM images. The microscale failure mechanisms, including matrix/particle debonding, particle cracking, and matrix ductile failure was quantified based on the fraction of fully debonded interfacial nodes, fraction of cracked particles, and the crack volume fraction, respectively, by using Python scripting in Abaqus. The validated model was leveraged to study the effect of Al2O3 particle size and weight fraction, and the stress state on the material behavior. Our analysis based on the quantification of damage mechanisms revealed that the sequence of the activation of failure mechanisms is consistent across different stress states: I. matrix/particle debonding, II. particle cracking, and III. matrix ductile failure. Altogether, the present work provides a better understanding of the evolution of failure in the material and this has implications for the design and improvement of the material. Additionally, our quantitative damage analysis lays the foundation for developing micromechanismbased constitutive models for cold-sprayed additively manufactured metal-ceramic materials in the future.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".