Potential of cold spray technology for 3D printing of dense TiO2 green parts
Bibliographic record
Abstract
Manufacturing ceramic parts, generally involves forming a green body and heating it to fuse the particles together, thereby densifying the part. Methods such as powder compaction in a die cavity using mechanical pressing, slip casting, and injection molding are widely employed in the industry for green body formation. However, these methods are costly, complex, and time-consuming. Recently, various 3D printing technologies have been adapted for manufacturing ceramic parts. These include slurry-based methods such as stereolithography and inkjet printing, powder-based systems like selective laser sintering/melting and binder jetting, and bulk solid-based methods such as laminated object manufacturing and fused deposition modeling. Regardless of the forming technology, the green density of the printed preform is a vital factor in achieving fully dense parts with high dimensional accuracy after firing. Among all these methods, injection molding achieves the highest green density, ranging from 75% to 85%, while 3D printing methods typically achieve green densities between 40% and 60%. Therefore, post-processing techniques such as cold isostatic pressing are often employed to enhance the density of parts up to 85%. In this research, we evaluate cold spray technology potential for 3D printing green TiO2 parts with exceptionally high green density. By leveraging the supersonic impact of TiO2 agglomerates, we demonstrate the ability to fabricate dense 3D structures with green densities ranging from 70% to 80%. Subsequent sintering further increases the density to well over 96.2%. This approach eliminates the need for powder modification, allowing as-received powders to be directly fed into the system for free-forming 3D printing, offering significant technical and cost advantages over conventional 3D printing techniques. These attributes can position cold spray as a promising manufacturing method in digital printing technologies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".