Development of a Technique for Toughness Estimation in Dual-Phase Steels Using Representative Volume Elements
Bibliographic record
Abstract
A novel approach to estimating the absorbed energy (toughness) in a uniaxial tensile test with only knowledge of the microstructure is presented. The flow behavior of each Dual-Phase (DP) steel grade is predicted using idealized Representative Volume Elements (RVEs) up to uniform elongation. To estimate the flow behavior beyond uniform elongation, the stress-modified fracture strain in a non-local damage model was implemented in Abaqus. Damage parameters were calibrated using Finite Element (FE) simulations of purely ferritic tensile specimens. The damage parameters remained unchanged, except for the coefficient of triaxiality. This coefficient was adjusted based on the average triaxiality of ferrite elements at the instability point of the uniaxially loaded RVEs for each DP steel grade. The proposed approach comprises two steps: micron-sized RVEs to predict the flow behavior up to the point of uniform elongation and the average triaxiality and full-scale tensile-test simulations to predict the rest of the curves. The results show that the damage parameters calibrated for high-strain ferrite effectively estimate the absorbed energy during failure in tension tests. This approach is also geometry-independent; varying the geometry of the tensile specimen, including miniature or notched specimens, still yields predicted absorbed energies that are in good agreement with the experimental results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".