Numerical–Experimental Analysis toward the Strain Rate Sensitivity of 3D-Printed Nylon Reinforced by Short Carbon Fiber
Bibliographic record
Abstract
Despite the application of the Additive Manufacturing process and the ability of parts’ construction directly from a 3D model, particular attention should be taken into account to improve their mechanical characteristics. In this paper, we present the effect of individual process variables and the strain-rate sensitivity of Onyx (Nylon mixed with chopped carbon fiber) manufactured by Fused Filament Fabrication (FFF), using both experimental and simulation manners. The main objective of this paper is to present the effect of the selected printing parameters (print speed and platform temperature) and the sensitivity of the 3D-printed specimen to the strain rate during tensile behavior. A strong variation of tensile behavior for each set of conditions has been observed during the quasi-static tensile test. The variation of 40 °C in the platform temperature results in a 10% and 11% increase in Young’s modulus and tensile strength, and 8% decrease in the failure strain, respectively. The variation of 20 mm·s−1 in print speed results in a 14% increase in the tensile strength and 11% decrease in the failure strain. The individual effect of process variables is inevitable and affects the mechanical behavior of the 3D-printed composite, as observed from the SEM micrographs (ductile to brittle fracture). The best condition according to their tensile behavior was chosen to investigate the strain rate sensitivity of the printed specimens both experimentally and using Finite Element (FE) simulations. As observed, the strain rate clearly affects the failure mechanism and the predicted behavior using the FE simulation. Increase in the elongation speed from 1 mm·min−1 to 100 mm·min−1, results in a considerable increase in Young’s modulus. SEM micrographs demonstrated that although the mechanical behavior of the material varied by increasing the strain rate, the failure mechanism altered from ductile to brittle failure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".