Thermo-mechanical improvement of Inconel 718 using ex situ boron nitride-reinforced composites processed by laser powder bed fusion
Bibliographic record
Abstract
Abstract Hexagonal boron nitride-reinforced Inconel 718 (h-BN/IN718) composites were fabricated using a laser powder bed fusion (LPBF) technique to treat a nanosheet-micropowder precursor mixture prepared in a mechanical blending process. Tailoring the BN in IN718 enhanced the thermal resistance of the composites, thereby dampening the sharpness of the melting temperature peak at 1364 °C. This is because the presence of the BN reinforcement, which has a low coefficient of thermal expansion (CTE), resulted in a heat-blocking effect within the matrix. Following this lead, we found that the BN (2.29 g/cm 3 ) was uniformly distributed and strongly embedded in the IN718 (8.12 g/cm 3 ), with the lowest alloy density value (7.03 g/cm 3 ) being obtained after the addition of 12 vol% BN. Consequently, its specific hardness and compressive strength rose to 41.7 Hv 0.5 · cm 3 /g and 92.4 MPa · cm 3 /g, respectively, compared to the unreinforced IN718 alloy with 38.7 Hv 0.5 · cm 3 /g and 89.4 MPa · cm 3 /g, respectively. Most importantly, we discovered that the wear resistance of the composite improved compared to the unreinforced IN718, indicated by a decrease in the coefficient of friction (COF) from 0.43 to 0.31 at 2400 s. This is because the BN has an exfoliated surface and intrinsically high sliding and lubricating characteristics.
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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".