Crystallographic texture and precipitation control via double austenitization in high-performance tool steel
Bibliographic record
Abstract
In the present work, the toughness and wear resistance of AISI A8 cold work high-strength tool steel were improved through the design of a specific double austenitization and tempering heat treatment. The treatment resulted in 80 % improvement in impact toughness and 10 % improvement in wear resistance. A combination of high-resolution dilatometry and scanning electron microscopy, including phase compositional and crystallographic analysis, was used to identify the microstructural changes. The findings were analyzed in terms of the influence of the proposed heat treatment on the refinement of the as-quenched martensite substructure and the fraction of high-angle grain boundaries (V1/V2, V1/V3 and V1/V6) variant pairs and the fraction of low-angle grain boundaries (V1/V4) pairs. Notably, block width analysis revealed that in the double austenitization treatment, block width appeared insensitive to prior austenite grain size variations. Additionally, the kinetics of the M 23 C 6 secondary carbides precipitation during isothermal tempering at 520 °C was investigated and modeled. Results demonstrate that the redistribution of carbon in solid solution is the critical microstructural parameter influencing the variant selection and the homogenous precipitation of spherical carbides. The underlying micro-mechanisms responsible for such improvement were identified and their contributions documented and quantified.
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".