Effect of Coating Materials on the Fatigue Behavior of Hip Implants: A Three-dimensional Finite Element Analysis
Bibliographic record
Abstract
This study aims to validate, using finite element analysis (FEA), the design concept by comparing the fatigue behavior of hip implant stems coated with composite (carbon/PEEK) and polymeric (PEEK) coating materials corresponding to different human activities: standing up, normal walking and climbing stairs under dynamic loadings to find out which of all these models have a better performance in the prosthesis-bone systems. A 3D finite element models of hip implants, femur, coating layers with polymeric (PEEK) and composite (carbon/PEEK) coating materials are created for FEA. The cyclic loads are applied on the prosthesis head. Fatigue life durations are calculated based on the Goodman mean-stress fatigue theory. The fatigue safety factor for the coated implant is increased more than 12.73% at least compared to the uncoated implant. The carbon/PEEK composite material with 0, +45, -45, and 90 degrees fiber orientation (configuration I) has the highest fatigue life and fatigue safety factor. The numerical result show that the carbon/PEEK composite material (configuration I) seems to be a good solution to increase the values of fatigue safety factor of coating layers due to highest fatigue life and fatigue safety factor. It distributes the applied load and transfers it to the bone, reducing stress-shielding effects and prolong the bone-prosthesis system life span.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".