Modeling of Effect of Infill Density Percentage on Rotating Bending Fatigue Behavior of Additive-Manufactured PLA Polymers
Bibliographic record
Abstract
Nowadays, 3D PLA-printed parts are widely used in many applications, essentially using the fused filament fabrication technique. While the influence of printing parameters on quasi-static mechanical characterization has been extensively considered within the literature, there are limited accounts of this effect on fatigue performance. The two main aims of this research are first to investigate the effects of the infill density percentage on the fatigue life of dog-bone samples under rotating bending cycling loads, and second to model the fatigue life using Wöhler and Basquin models. The experiments exhibit a high variability of results, especially for low cyclic loads. The S-N curves show that the number of cycles at failure increases with the increase in the infill density percentage and decreases with the increase in loads. Investigations allow the formulation of each constant model as a function of the infill density percentage. The new fatigue model formulations exhibit good agreement with the experimental data. As an outcome of this study, the fatigue model for 3D-printed parts may be expressed as a function of the infill density percentage using fewer tests in the future and for other polymers used in fused filament fabrication.
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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 | 0.001 |
| 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 | 0.000 | 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".