Effect of Annealing Process on Microstructure, Texture, and Mechanical Properties of a Fe-Si-Cr-Mo-C Deep Drawing Dual-Phase Steel
Bibliographic record
Abstract
Dual phase steel generally has poor deep drawing property with a low r value less than 1.0, making it difficult to be used for deep drawing automotive parts. In order to improve the mechanical properties of the steel through heat treatment, effect of heat treatments with different conditions on a Fe-Si-Cr-Mo-C deep drawing dual-phase steel was investigated with the aim of identifying effective heat treatment parameters for effective modification towards optimal properties. Relevant thermal dilation and heat treatment experiments were performed. Corresponding characters were investigated. The results show that island martensite can be obtained at low cooling rate. With the increase of cooling rate, the formation of pearlite and bainite is favored. During annealing at low temperatures, recrystallization of the steel is incomplete with the presence of the shear bands. With the increase of annealing temperature, the recrystallization process is gradually complete, and the number of high angle grain boundaries increases significantly. The ratio of gamma orientation components to alpha orientation components decreases first and then increases with the increase of annealing temperature. The strain hardening exponent and r value show an upward trend with respect to annealing temperature, and the r value is as high as 1.15.
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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 | 0.000 |
| 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".