3D-Printed Ankle Foot Orthosis (AFO) with Optimized Material and Design for Children with Cerebral Palsy
Bibliographic record
Abstract
Cerebral palsy (CP) often causes mobility limitations that require assistive devices such as Ankle Foot Orthoses (AFOs) to enhance functional stability. This study aims to develop an optimized 3D-printed AFO design that improves comfort, structural durability, and production efficiency for children with CP. The research applies a Design of Experiment approach using the Taguchi method to optimize 3D printing parameters, supported by tensile testing to identify the best material configuration. Design alternatives were prioritized using the Analytical Hierarchy Process, while Finite Element Analysis was conducted to evaluate mechanical performance under physiological loading. The selected PETG configuration (33% infill density and 0.15 mm layer thickness) demonstrated improved tensile strength and flexibility, contributing to enhanced structural behavior. A prototype was produced and validated using the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) questionnaire. Results showed higher overall user satisfaction for the optimized 3D-printed AFO compared to conventional devices, particularly in safety, comfort, and durability. The integration of optimized material parameters, systematic design evaluation, and user-centered assessment provides an effective pathway toward improving AFO performance and supporting the mobility and quality of life of children with cerebral palsy.
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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 | 0.000 |
| Research integrity | 0.000 | 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".