Experimental Evaluation and Finite Element Analysis of Stress Distribution in 3D-Printed Dental Implants to Validate the Optimal Thread Pitch
Bibliographic record
Abstract
Objective: As an alternative method of prosthetic rehabilitation, the dental implant serves as one of the solutions that may be used to restore a missing tooth over a lengthy period of time.In order to achieve stress concentration in 3D printed 30% CFR PEEK dental implants, the finite element approach and the experimental photoelasticity test are applied.This is done in order to find the optimal thread pitch for a 3D printed 30% CFR PEEK implant and its impact on the bone implant interface.Materials and methods: Three-dimensional models were generated for the one-piece implant and bone structures.The models were created by introducing variations of 0.8mm, 1mm, and 1.2mm in the thread pitch while maintaining a fixed implant length of 13mm.SolidWorks software was employed for the creation of these models.Subsequently, the stress distribution of the models was simulated under axial load using ANSYS software.An experimental model was created utilizing 3D printing technology, and further experimental tests were conducted to assess the stress concentration in dental implants-bone interfaces.These evaluations were performed using the photoelasticity test method.Results: As a result of the findings, it seems that the implant, cortical, and cancellous bones all exhibit different levels of stress intensity.A thorough analysis of the stress intensity is used to establish the optimal configuration for the pitch of the components and the behavior of 3D printed implant in cancellous bone.Conclusions: After careful consideration, it has been shown that the current finite element model adequately forecasts the stress concentration pattern of dental implants.In light of the fact that the findings of the FEM test are more accurate than those of the photoelasticity test, it is recommended that computation techniques be used in medical practice since they have tremendous potential for new research.This research suggested that the optimum ranges for the length and pitch of the 3D printed 30% CFR PEEK implant are determined to be 13mm and 0.8mm, respectively and a low implant thread pitch results in a reduction in stress concentration at the implantcancellous bone interface.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".