Molybdenum 8wt% rhenium alloy processed by laser powder bed fusion: From powder production to mechanical testing at elevated temperatures
Bibliographic record
Abstract
Molybdenum is highly valued in industry because of its unique properties, especially at high temperatures. Additive manufacturing technologies , particularly laser powder bed fusion (LPBF), are becoming increasingly popular for producing complex shapes at a lower cost as compared to the conventional forming processes. However, printing molybdenum with LPBF presents challenges, especially caused by its hot cracking susceptibility . Several approaches have been explored to address this issue, including alloying molybdenum with other elements, which has proven effective in enhancing the printability and minimizing the occurrence of cracking, particularly with the addition of rhenium . In this study, a combination of mechanical blending of molybdenum powder and a rhenium precursor, followed by the reduction of the precursor and plasma spheroidization, was used to produce spherical 30–55 μm molybdenum‑rhenium (8 wt%) powders with rheological properties suitable for LPBF. Compared to pure molybdenum, the use of the alloyed powder led to an increase in the crack-free printed density, from 97 to 98.5 %, and in the compressive strength , from 240 to 340 MPa, at 600 °C and from 150 to 190 MPa at 1000 °C, at the expense of a ∼ 5 % reduction in the compression strain . To demonstrate the potential of printing complex geometries using the developed powders, complex geometry artifacts containing 0.25 mm-thin letters, wide dense sections and auto-supported 50 %-density 0.7 mm-thin strut diamond lattice structures were successfully printed.
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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".