Influence of deformation path on microstructure evolution during multi-step deformation of a high strength steel: experiments and FE analysis
Bibliographic record
Abstract
This study aims to investigate the effect of deformation path on deformation and microstructure evolution during the multi-step deformation of a high strength steel. The deformation paths were illustrated by flat and concave surface anvils. Multi-step deformation experiments were conducted on high strength steel specimens using thermomechanical simulator, Gleeble 3800 equipped with MaxStrain module, a special purpose attachment. All tests were performed at a strain rate and temperature of 0.01 s⁻¹ and 1150 °C respectively, considering two deformation paths. A total true strain of 0.84 was imparted over four steps, with approximately 0.21 strain applied per step. The results were used to analyze the influence of varying deformation paths on microstructure evolution and hardness distribution. A finite element (FE) model was developed using Forge NxT 3.2 FE code to simulate the multi-step deformation process. The strain distribution obtained via FE model was correlated to the average grain size and hardness distribution after the multi-step deformation experiments for validation. This validated FE model was capable of predicting strain distribution, dynamic recrystallization (DRX) volume fraction, and grain size evolution during the multi-step deformation process. A comparative study of results obtained from two deformation paths was conducted to determine the optimal deformation path, aiming homogeneous strain and grain size distribution. The Coefficient of Variation (CoV) was used to assess the heterogeneity of the hardness distribution. The results suggest that concave anvils promote a higher and more uniform strain distribution, leading to a homogeneous distribution of grain size and hardness. An increased and more uniform strain distribution promotes complete dynamic recrystallization (DRX) with finer grain size leading to homogeneous hardness distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".