Analysing strength, hardness and grain-structure of 0.2%-C steel specimens processed through an identical heating period with different continuous transformation rates
Bibliographic record
Abstract
Abstract The present work deals with improvement of mechanical properties and refining the microstructure of low carbon steel (0.2%-C) after applying heat treatment techniques. For the purpose, five different samples were taken under study. First sample was kept in ‘as received’ condition and other four samples were undergone into heating process in an Induction furnace. The holding temperature of all the four samples were kept common i.e., 850 °C for a fixed period of 2.5 h. Then, these four samples were cooled into four different cooling media i.e., Air, Water, Oil, and Furnace. All the samples were in the form of rods with 195 mm length and 32 mm diameter. The universal testing machine was used to determine the tensile strength of all the samples. Rockwell hardness tester was used to find the hardness of samples. The microstructural variation was analysed through an optical microscope. All the results were analysed and compared with ‘as received’ sample. The Oil cooled sample showed the highest tensile strength of 585 MPa. The microstructural orientation of oil cooled sample i.e., bainite + fine lamella of ferrite and cementite, provides a good hardness, strength, and toughness to the steel. In addition, XRD and fractography analysis of the samples were also carried out.
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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) | 0.003 | 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".