Quasi‐static strength and fractography analysis of two dental implants manufactured by direct metal laser sintering
Bibliographic record
Abstract
BACKGROUND: New manufacturing methods was developed to improve the tissues integration with the titanium alloy pieces. OBJECTIVE: The present in vitro study was to assess the resistance and fracture mode after applied a quasi-static compressive force on the two dental implants manufactured by direct metal laser sintering. MATERIALS AND METHODS: Twenty dental implants manufactured by direct metal laser sintering, using titanium alloy (Ti-6Al-4V) granules in two designs (n = 10 per group): Conventional dental implant (group Imp1) two-piece implant design, where the surgical implant and prosthetic abutment are two separate components and, the one-piece implant (group Imp2), where the surgical implant and prosthetic abutment are one integral piece. All samples were subjected to quasi-static loading at a 30° angle to the implant axis in a universal testing machine. RESULTS: The mean fracture strengths were 1269.2 ± 128.8 N for the group Imp1 and, 1259.5 ± 115.1 N for the group Imp2, without statistical differences (P = .8722). In both groups, the fracture surface does not present crack between the compact core and the superficial (less dense and porous) part of the implants. CONCLUSIONS: Based on the measured resistance data for the two implant models manufactured by direct metal laser sintering tested in the present study, we can suggest that they have adequate capacity to withstand the masticatory loads.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".