Removable partial denture alloys processed by laser‐sintering technique
Bibliographic record
Abstract
Removable partial dentures (RPDs) are traditionally made using a casting technique. New additive manufacturing processes based on laser sintering has been developed for quick fabrication of RPDs metal frameworks at low cost. The objective of this study was to characterize the mechanical, physical, and biocompatibility properties of RPD cobalt-chromium (Co-Cr) alloys produced by two laser-sintering systems and compare them to those prepared using traditional casting methods. The laser-sintered Co-Cr alloys were processed by the selective laser-sintering method (SLS) and the direct metal laser-sintering (DMLS) method using the Phenix system (L-1) and EOS system (L-2), respectively. L-1 and L-2 techniques were 8 and 3.5 times more precise than the casting (CC) technique (p < 0.05). Co-Cr alloys processed by L-1 and L-2 showed higher (p < 0.05) hardness (14-19%), yield strength (10-13%), and fatigue resistance (71-72%) compared to CC alloys. This was probably due to their smaller grain size and higher microstructural homogeneity. All Co-Cr alloys exhibited low porosity (2.1-3.3%); however, pore distribution was more homogenous in L-1 and L-2 alloys when compared to CC alloys. Both laser-sintered and cast alloys were biocompatible. In conclusion, laser-sintered alloys are more precise and present better mechanical and fatigue properties than cast alloys for RPDs. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 1174-1185, 2018.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".