Towards optimization of polymer filament tensile test for material extrusion additive manufacturing process
Bibliographic record
Abstract
Material extrusion (MEX) is a popular additive manufacturing (AM) method that can process a wide range of feedstock materials, most commonly in filament form. Currently, there is no standardized testing method for filament tensile properties, and researchers resort to 3D-printed dog-bone specimens, which necessarily include the effects of the printing process. In this study, the impact of the strain measurement device, knife-edge type, gage length, testing speed, and oven treatment on filament tensile properties was explored using an off-the-shelf fixture. It was observed that an extensometer with blunt knife edges, a filament gage length of 165 mm, and a 6.35 mm/min (0.25 in./min) testing speed could accurately evaluate the tensile properties of acrylonitrile butadiene styrene (ABS) filaments. In addition, an optimized raster path, 3D printing design, and process parameters were used to manufacture dog bone tensile specimens according to ASTM D638-22 from the same ABS filament spool. The tensile properties of the filaments were validated using the results of 3D-printed dog-bone specimens. Young's modulus, stress at yield, and stress at break for the optimum filament test set (2.20 GPa, 43.9 MPa, and 39.1 MPa) were very similar to those of the 3D-printed specimens (2.26 GPa, 44.9 MPa, and 37.3 MPa). The optimum filament tensile testing parameters determined in this study for ABS can be used for the initial test setup for other filament materials to provide baseline values that can serve as the foundation for AM process development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".