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.
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 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 | 0.000 |
| 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) | 0.000 | 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".