Development of a Novel Tape-Casting Multi-Slurry 3D Printing Technology to Fabricate the Ceramic/Metal Part
Bibliographic record
Abstract
Printing ceramic/metal parts increases the number of applications in additive manufacturing technology, but printing different materials on the same object with different mechanical properties will increase the difficulty of printing. Multi-material additive manufacturing technology is a solution. This study develops a novel tape-casting 3D printing technology that uses bottom-up photopolymerization to fabricate the green body for low-temperature co-fired ceramics (LTCC) that consist of ceramic and copper. The composition of ceramic and copper slurries is optimized to allow printing without delamination and sintering without cracks. Unlike traditional tape-casting processing, the proposed method deposits two slurries on demand on a transparent film, scrapes it flat, then photopolymerization is induced using a liquid crystal displayer to project the layer pattern beneath the film. The experimental results show that both slurries have good bonding strength, with a weight ratio of powder to resin of 70:30, and print a U-shaped copper volume as a circuit within the LTCC green body. A three-stage sintering parameter is derived using thermogravimetric analysis to ensure good mechanical properties for the sintered part. The SEM images show that the ceramic/copper interface of the LTCC sintered part is well-bonded. The average hardness and flexural strength of the sintered ceramic are 537.1 HV and 126.61 MPa, respectively. Volume shrinkage for the LTCC slurry is 67.97%, which is comparable to the value for a copper slurry of 68.85%. The electrical resistance of the printed copper circuit is 0.175 Ω, which is slightly greater than the theoretical value, hence it has good electrical conductivity. The proposed tape-casting 3D printer is used to print an LTCC benchmark. The sintered benchmark part is validated for the application in the LTCC application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".