Rate dependence of high strength steels mechanical behavior: Characterization, modelling and impact applications
Bibliographic record
Abstract
Strain rate dependence is a critical factor in understanding the mechanical behavior of materials, particularly high-strength steels, which are widely used in safety-critical applications due to their superior strength, toughness and energy absorption under dynamic loading conditions. These properties make them ideal candidates for use in scenarios such as ballistic impacts, where materials are subjected to extreme deformation rates. Despite extensive research on individual material systems, or models, a comprehensive review that integrates experimental observations, material characterization techniques and constitutive modeling across different strain rates regimes remain lacking. This review addresses that gap by evaluating strain rate-dependent behavior in high-strength steels, while also highlighting how many of the underlying mechanisms and modeling approaches are applicable to other steel categories and metallic alloys. In addition to reviewing key constitutive models, we discuss the experimental techniques used to characterize rate-dependent behavior, such as Split-Hopkinson Pressure Bar (SHPB) testing and high-speed digital image correlation. The comparison reveals that while the Johnson-Cook model is widely used for its simplicity, the GISSMO model offers improved accuracy for capturing stress-state-sensitive damage evolution in high-strength steels. Furthermore, we emphasize the necessity of microscale analysis, particularly through the Crystal Plasticity Finite Element Method (CPFEM), which has shown strong potential for predicting deformation sequences and crack initiation in complex loading conditions. Overall, this review provides an organized overview of strain rate regimes, their associated macroscopic and microscopic behaviors, the experimental methods used to characterize them, and the modeling strategies suited for different steel systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".