Design and fabrication of cast orthopedic implants with freeform surface textures from 3-D printed ceramic shell
Bibliographic record
Abstract
Three-dimensional printing is a solid freeform fabrication process, which creates parts directly from a computer model. The parts are built by repetitively spreading a layer of powder and selectively joining the powder in the layer by ink-jet printing of a binder material. 3D printing was applied to the fabrication of sub-millimeter surface textures with overhang and undercut geometries for use in orthopedic prostheses as bony ingrowth structures. 3D printing is used to fabricate ceramic molds of alumina powder and silica binder, and these molds are used to cast the bony ingrowth surfaces of Co-Cr (ASTM F75) alloy. Minimum positive feature sizes of the ceramic mold and, therefore, minimum negative feature sizes of castings were determined to be approximately 200 x 200 x 175 microm and were limited by the strength of ceramic needed to withstand handling. Minimum negative feature sizes in the ceramic mold and, therefore, minimum positive features in the casting were found to be approximately 350 x 350 x 175 microm and were determined by limitations on removal of powder from the ceramic and the pressure required to fill these small features with molten metal during casting. Textures were designed with 5 layers of distinct geometric definition, allowing for the design of overhung geometry with overall porosity ranging from 30-70%. Features as small as 350 x 350 x 200 microm were included in these designs and successfully cast.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".