Laser powder bed fusion of Alumina/Fe–Ni ceramic matrix particulate composites impregnated with a polymeric resin
Bibliographic record
Abstract
Additive Manufacturing (AM) plays a key role in meeting the vital demands of Industry. The AM industry needs the range of applicable materials to be expanded by conducting research on novel ones. In the present investigation, alumina/Fe–Ni (steel) ceramic matrix particulate composite was fabricated employing laser powder bed fusion (LPBF) additive manufacturing (AM) technology. The quality of the printed samples was associated with the LPBF process parameters, which were optimized for this process. In general, the fabricated samples showed a microstructure of alumina matrix with uniform distribution of steel (Fe–Ni) particles. The as-printed samples exhibited pores. Thus, they were subjected to a sintering heat treatment cycle under an inert atmosphere. Although the sintering cycle considerably increased the average Vickers hardness, pores were not eliminated entirely. Therefore, polymer impregnation of the as-sintered samples was carried out to reduce porosities and microcracks. The mercury porosimeter showed that the porosity decreased sequentially after sintering and polymer impregnation. In addition, mechanical investigations revealed that the polymer impregnation improved the compressive strength of the sintered samples (from 56 to 120 MPa). Alumina-based materials find wide applications in various fields, including the manufacturing of electronic components, cutting tools, biomedical implants, and catalyst converters, owing to their low density, high hardness, wear and corrosion resistance, and biocompatibility. This study presents a viable approach for the fabrication of these materials, with developed samples exhibiting promising properties. The study emphasizes the potential of additive manufacturing as an approach for the fabrication of ceramic matrix composites reinforced with metallic particulates in future research.
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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) | 0.000 | 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 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".