Hybrid experimental–numerical study on nanoindentation response of dual-phase steels: From macroscale to atomic scale
Bibliographic record
Abstract
In this study, the elastic and plastic behaviors of the ferrite and martensite phases of dual-phase (DP) steels were investigated using nanoindentation data and the related constitutive equations. First, hardness (H) and elastic modulus (E) were determined to derive the monotonic yield stress (σ y ) and Hollomon's parameter and then for work hardening exponent (K) and work hardening rate (n).Next, the results obtained by the nanomechanical approach implemented herein were validated using the semiquantitative data computed by numerical finite element analysis (FEA) and molecular dynamics (MD). The difference between plasticity of ferrite and martensite can be attributed ti the geometrically necessary dislocations (GNDs), which stimulate work hardening. The elastic and plastic data of both the phases were incorporated into FEA to simulate the load–displacement curves and the projected regions. In addition, the load–displacement curves of the ferrite and martensite phases and the hardness and Young's modulus determined by MD were in good agreement with the nanoindentation test and FEA results. The strain-rate sensitivity of ferrite, which exhibited a lower hardness and greater indentation depth, was 0.0985, whereas that of martensite was approximately 0.087. Furthermore, the TEM images proved the existence of GNDs at the ferrite–martensite interface and their role in cell formation in the ferrite zone and interphase region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".