Workflow Development of a 3D Printed Novel Implant Abutment
Bibliographic record
Abstract
Dental implant components, including titanium abutments and superstructures, are currently fabricated through subtractive manufacturing. This investigation explored an additive manufacturing workflow using titanium for the fabrication of a novel dental implant abutment. The novel abutment was designed, patented, digitally refined, and printed in dental-grade titanium Ti64 (titanium 6-aluminum 4-vanadium) using selective laser melting technology. Numerous iterations of the abutment were designed, printed, and evaluated to determine the final optimized design for additive manufacturing. Postprocessing involved bead blasting, fixation with a custom stabilization jig, and manually creating threads using a die. The coupling of the abutment with the implant body was suitable, as assessed under magnification and through radiological assessment. Physical testing of the abutment has been completed. Data indicate that the component can withstand the recommended torque and strength required for provisionalization. The additive manufacturing pathway for abutment fabrication presents an efficient, cost-effective, and customizable workflow.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".