Computational evaluation of interfacial fracture toughness of thin coatings
Bibliographic record
Abstract
A computational method to evaluate fracture toughness of single-and multilayered coatings using first-principles density functional theory (DFT) calculations was proposed.This method was first applied to calculate elastic properties and fracture toughness K IC of single crystalline TiC and several transition metal nitrides with cubic structure, such as TiN, CrN, ZrN, VN and HfN.After comparison with known experimental data and other DFT results, the reliability of present calculations was favourably confirmed.Next, DFT was applied to calculate the ideal work of adhesion W ad , Young's modulus E and interfacial fracture toughness K IC Int for bi-layer combinations of five transition metal nitrides in ( 100) and (110) surface orientations.For the analyzed coatings, the following trends were observed: E(100) > E(110), W ad (100) < W ad (110) and K IC Int (100) < K IC Int (110), demonstrating that it is the W ad that plays a decisive role in determining interfacial fracture toughness of these materials.All interfaces formed with TiN in the (110) orientation showed the best combination of adhesion and interfacial fracture toughness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".