Influence of Different Heat Treatments on Microstructure Evolution and High-Temperature Tensile Properties of LPBF-Fabricated H13 Hot Work Steel
Bibliographic record
Abstract
This study investigates the effect of tensile test temperatures, ranging from 300 °C to 600 °C, on the microstructure, mechanical properties, and fracture behavior of AISI H13 11 tool steel manufactured by laser powder bed fusion (LPBF) under three material conditions: As-Built (AB), Direct Double-Tempered (DTT), and 13 Quenched and Double-Tempered (QTT). Optical and SEM observations show that quenching before tempering leads to a more homogeneous microstructure. Full austenitization during quenching eliminates the laser track patterns and cellular structures characteristic of the AB and DTT conditions, resulting in a microstructure like that of conventionally processed material. Tensile test results reveal that, while all material conditions (AB, DTT, and QTT) perform similarly at lower temperatures (up to 300 °C), significant differences emerge at elevated temperatures. At 300 °C, AB, DTT, and QTT maintain 87.5%, 85.8%, and 83.1% of their room-temperature yield strength, respectively. However, beyond this point, the DTT condition clearly outperforms the others. QTT shows a sharp decline above 300 °C, retaining only ~24% of its yield strength, whereas AB and DTT maintain approximately 80%. The superior performance of DTT becomes more evident at higher temperatures: it retains 25% and 20% of its yield strength at 500 °C and 600 °C, respectively, higher than both AB and QTT.
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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".