Process-Structure-Property Relationships of AISI H13 Tool Steel Processed with Selective Laser Melting
Bibliographic record
Abstract
Due to a good combination of high hardness, wear resistance, toughness, resistance to high operating temperatures, and fairly low material cost, AISI H13 tool steel is commonly used in the manufacture of injection molds. Additive manufacturing (AM) such as selective laser melting (SLM), due to the layer-wise nature of the process, offers substantial geometric design freedom in comparison with conventional subtractive manufacturing methods, thereby enabling a construction of complex near-net shape parts with internal cavities like conformal cooling channels. The quality of SLM-manufactured parts mainly depends on the part geometry, build orientation and scanning strategy, and processing parameters. In this study, samples of H13 tool steel with a size of 10 × 10 × 15 mm3 were SLM-manufactured using a laser power of 100, 200, and 300 W; scanning speed of 200, 400, 600, 800, 1000, and 1200 mm/s; and hatch spacing of 80 and 120 µm. A constant layer thickness of 40 µm, 67° scanning rotation between subsequent layers, and a stripe scanning strategy were maintained during the process. The samples were built considering a preheating of 200 °C. The relative density, surface roughness, crack formation, microstructure, and hardness were evaluated. The relative density is shown to increase with increasing the volumetric energy density up to a value of about 60 J/mm3 and then no significant increase can be pointed out; the maximum relative density of 99.7% was obtained. A preheating of 200 °C generally aids to increase the relative density and eliminate the crack formation. The microstructure of built samples shows fine equiaxed cellular-dendritic structure with martensite and some retained austenite. The microhardness of the as-built samples was found to vary from 650 to 689 HV 0.2, which is comparable to a conventionally produced H13 tool steel.
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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 | 0.001 | 0.001 |
| 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".