Effects of Voids and Raster Orientations on Fatigue Life of Notched Additively Manufactured PLA Components
Bibliographic record
Abstract
Abstract In this study, the fatigue life of notched Polylactic acid (PLA) samples fabricated through the fused deposition modeling (FDM) technique was studied experimentally and numerically. The volumetric method based on the theory of critical distance was employed for fatigue life predictions. The effects of influential process parameters including raster orientation and FDM-induced defects such as voids or gaps inside the parts were examined on the fatigue strength reduction factors and fatigue lives. Circular and elliptical-shaped notch geometries were considered with various dimensions. Fatigue tests were conducted on notched and un-notched samples at the load ratio of 0.1. Predicted results were compared with experimental fatigue test data. Results revealed that the raster orientation parameter had a substantial impact on fatigue strength reduction factors and fatigue lives. The stress concentrations induced by the FDM process on the surfaces and inside the parts for the samples with 90º raster angles acted similar to the sharp notches, resulting in no substantial difference in fatigue life of notched and un-notched 3D printed samples. In contrast, un-notched 3D printed specimens with 0º raster orientations possessed higher fatigue lives as compared to the notched samples. While the volumetric approach efficiently predicted the fatigue lives of the samples with 90º raster orientations, it moderately underpredicted the fatigue lives of the samples with 0º raster angles.
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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 | 0.000 |
| 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) | 0.000 | 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".