Material Characterization of Fused Filament Fabrication for Lower-Limb Prosthetic Sockets
Bibliographic record
Abstract
Abstract Additive manufacturing (AM) is a very versatile and powerful tool for fabricating lower-limb prosthetic sockets. Fused filament fabrication (FFF), a material extrusion AM technique, has the potential to greatly improve the function and comfort of prosthetic sockets with enhanced features such as in-socket pressure monitoring or spatially varied material stiffness. Fiber-reinforced polymers (FRPs) are conventionally used to manufacture sockets that have been proven to provide adequate structural durability. However, evidence is lacking to show that sockets made with FFF have the required material properties, and the field is far from establishing a standard method for manufacturing and testing FFF sockets with acceptable strength and stiffness. In this paper, material characteristics of FFF and FRP specimens obtained from ASTM D3039 standard tensile testing are compared and analyzed. Results showed that the tensile modulus of FFF specimens is not greatly affected by build orientation, and, in general, the printed layer heights tested in this study did not have an effect on modulus nor tensile strength. Longitudinal raster orientations significantly increased the tensile strength of FFF specimens built in the flat orientation, although all FFF specimens still exhibited significantly lower strength and modulus compared to the FRP composites. This paper provides a basis for the selection of FFF process parameters for designing adaptable and smart sockets, and provides suggestions for the use of material characterization data in predicting and optimizing the structural safety of future novel prosthetic socket designs.
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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 | 0.000 | 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".