H13 tool steel-copper composite fabricated by laser powder bed fusion and melt infiltration for high thermal conductivity tooling applications
Bibliographic record
Abstract
A two-step fabrication process was successfully developed to produce a tool steel/copper (H13/Cu) interpenetrating phase composite (IPC) intended for improving heat management in mold and die applications. H13 functionally graded lattice structures (FGLS) were first fabricated via laser powder bed fusion (LPBF) additive manufacturing (AM). X-ray diffraction (XRD) analysis of the as-built H13 FGLS revealed a predominantly lath martensite microstructure with 16% retained austenite (RA). The microstructure transformed into a fully tempered martensitic phase through a heat treatment, which involved air quenching at 1020 ºC and tempering at 500 ºC for 2 h (QT500). The subsequent infiltration of the H13 lattice with Cu, followed by the QT500 heat treatment, yielded a H13/Cu IPC with a relative density of 99.8%. The H13/Cu interface analysis indicated complete wettability, minimal α(Fe,Cr) precipitates in the Cu matrix (< 2%), and Cu penetration into H13 struts to a depth of 47 ± 7 μm. The microstructure and microhardness of the H13 after Cu infiltration followed by QT500 remained similar to the QT500 reference sample. The effective thermal conductivity ( k e ) of the composite was highly tailorable by adjusting the H13 lattice density, with measured k e values ranging from 145.2 ± 0.7 Wm -1 K -1 to 34 .1 ± 0.1 Wm -1 K -1 for H13 volume fractions of 40% to 90%, respectively. These measurements aligned well with predictions from the Torquato approximation and finite element modeling, confirming the potential to customize thermal properties for specific tooling applications.
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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".