Determination and verification of Johnson-Cook dynamic constitutive model for surface-modified layer of carburized 18CrNiMo7-6 alloy steel
Bibliographic record
Abstract
In the aerospace industry , various surface modification techniques are frequently employed to fabricate surface-modified layers (SMLs) on critical components, with the aim of enhancing the functionality and prolonging the operational lifespan. However, studies on the mechanical properties of SMLs are limited. This paper proposes a theoretical and experimental method based on layer-by-layer inversion for determining the Johnson–Cook (J–C) dynamic constitutive model parameters of SMLs via layer stripping. To establish the stress–strain relationships at varying carburization depths, strain rates , and temperatures, quasi-static compression tests , split Hopkinson pressure bar (SHPB) dynamic impact compression tests, and high-temperature, quasi-static, tensile tests were performed. Using the layer-by-layer inversion approach, the J–C dynamic constitutive parameters for each gradient layer of the SML were obtained, and a functional relationship between the depth of the SML and the J–C dynamic constitutive parameters was established. Finally, the accuracy of the derived J–C dynamic constitutive model parameters in predicting the dynamic compression behavior of carburized 18CrNiMo7-6 alloy steel SMLs was verified through finite element simulations combined with high-temperature, dynamic compression experiments. This study offers a novel approach for determining the J–C dynamic constitutive model parameters in plastically nonlinear gradient materials.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".