Fatigue life prediction of ABS/Graphene nanoplatelets 3D-printed composite parts
Bibliographic record
Abstract
A computational model has been developed to predict the fatigue life of Acrylonitrile Butadiene Styrene (ABS)/Graphene Nanoplatelet (GNP) composite parts produced using Fused Filament Fabrication (FFF) technique. This model accounts for key factors such as raster angles, GNP content in the filaments, and material degradation during cyclic loading. Fatigue life predictions were performed using two strain-based models: the modified Morrow model and the Smith-Watson-Topper (SWT) model. To address the effects of internal defects in 3D-printed parts, the model employed an innovative approach, treating the defected parts as homogeneous, defect-free components but with an imaginary notch and associated notch strength reduction factors at various load levels. The results demonstrated that this method effectively predicts fatigue life of FFF-processed 3D-printed parts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".