Optimization of Printing Parameters for Extrusion 3D Printing of Ceramic Clay
Bibliographic record
Abstract
With substantial advancements in the additive manufacturing (AM) of polymers and metals in the last few decades, the AM of ceramic material has recently gained research traction. Manufacturing complex ceramic parts is an extremely challenging process that can be revolutionized by AM. While many industrial-grade expensive AM systems are available for printing ceramic parts, the use of inexpensive desktop-type extrusion AM systems for ceramic parts is particularly challenging, which mandates printing parameter optimization. In this paper, the printing parameters of an inexpensive extrusion AM system, such as layer height, line width, extrusion speed, infill, and flow percentage, were optimized, which resulted in ceramic parts with desired qualities in terms of part density. For this, Clay Body 370 was chosen to produce clay with different percentages of water content ranging from 5% to 15%. Printed samples were subjected to sintering in a furnace to sinter at 1100 °C, followed by shrinkage and weight loss measurements. Finally, the printing parameters that resulted in good surface quality and less dimensional shrinkage with higher compression strength were reported.
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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 | 0.000 |
| 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 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".