HOW INFILL PERCENTAGE AFFECTS THE ULTIMATE STRENGTH OF A 3D-PRINTED TRANSTIBIAL SOCKET
Bibliographic record
Abstract
INTRODUCTION 3D printing for non‐weight‐bearing upper extremity prostheses is becoming increasingly popular as a method of fabrication.1 Some clinics in North America have begun using 3D printing to fabricate lower extremity diagnostic sockets (Figure 1). The strength requirements for upper extremity prostheses are not as rigorous as the strength requirements for lower extremity prostheses. Therefore, strength testing on 3D-printed lower extremity sockets is one of the first steps that needs to be conducted to ensure patient safety. 3D-printed prosthetic sockets are becoming an alternative option to traditional methods because it is possible to customize different parameters to create a strong structure. Infill percentage is an important parameter to research as this can have an influence on the strength of 3D printed sockets.2 As both prosthetists and healthcare professionals, there is a need to become more involved in the process of designing and testing 3D printed sockets. The purpose of this study is to test how changing the infill percentage affects the ultimate strength of a 3D printed transtibial socket during initial contact. Abstract PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/32038/24453 How to cite: Campbell L, Lau A, Pousett B, Janzen E, Raschke S.U. HOW INFILL PERCENTAGE AFFECTS THE ULTIMATE STRENGTH OF A 3D-PRINTED TRANSTIBIAL SOCKET. CANADIAN PROSTHETICS & ORTHOTICS JOURNAL, VOLUME 1, ISSUE 2, 2018; ABSTRACT, POSTER PRESENTATION AT THE AOPA’S 101ST NATIONAL ASSEMBLY, SEPT. 26-29, VANCOUVER, CANADA, 2018. DOI: https://doi.org/10.33137/cpoj.v1i2.32038 Abstracts were Peer-reviewed by the American Orthotic Prosthetic Association (AOPA) 101st National Assembly Scientific Committee. http://www.aopanet.org/
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".