Investigation of strain-hardening characteristics of cold-sprayed Al–Al <sub>2</sub> O <sub>3</sub> coatings: a combined nanoindentation and expanding cavity models approach
Bibliographic record
Abstract
The paper has taken a fundamental approach to study the nano-scale deformation behavior of Al-Al2O3 cermet coatings deposited by low-pressure cold spraying (LPCS) on AZ31 magnesium and Al6056 lightweight alloy substrates. Coating microstructural characteristics were first evaluated and correlated with LPCS process parameters using metallurgical characterization techniques: SEM, 3D optical profilometry, and XRD, followed by their microhardness and wear depth measurements and comparing with uncoated substrates under three-body abrasion wear. These properties were analyzed/mapped against probable deformation scenarios for nano-scale yield strength determination using the combined experimental nanoindentation load-depth curve method and computational expanding cavity models (ECMs). Obtained yield strength with key coating parameters like hardness and Young’s modulus were taken for modeling and simulation of strain-hardening effect under a peak loading of 165 mN in ABAQUS finite element (FE). Results from both combined experimental/computational and FE approaches indicate a progressive elasto-plastic mode being the dominating coating deformation mechanism with a strain hardening exponent of 0.15, under the studied loads.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".