Preliminary Insight Into Torsion of Additively-Manufactured Polylactic Acid (PLA)-Based Polymers
Bibliographic record
Abstract
Abstract Background Polymers in practical applications often face diverse torsional loads, such as polymeric gears, couplings, scaffolds, etc. Meanwhile, additive manufacturing enables the creation of intricate geometries for specific needs and its application to fabricate various component parts has grown exponentially. Nevertheless, research on cyclic and reversed cyclic torsional loading of additively-manufactured polymers is very limited. Objective Mechanical characterization of monotonic, cyclic, and reversed cyclic torsion in polylactic acid (PLA), PLA Premium, and PLA Tough materials. Methods Specimens were 3D-printed with a 0° build orientation using an extrusion technique and two infill orientation angles (± 45° and 0°/90°). Specimens were subjected to underwent monotonic, cyclic, and reversed cyclic torsion until failure. Results Regardless of material type, ductile fracture governed the behavior under monotonic loading and brittle failure under cyclic and reversed cyclic loadings. Specimens with a ± 45° infill orientation outperformed their 0°/90° counterparts across all materials, with PLA Premium exhibiting superior performance compared to PLA and PLA Tough. Importantly, it was demonstrated that the previously-proposed multilinear idealized shear stress-shear strain curve, developed for monotonic loading of 15 different polymers, also applies to the envelope curves of cyclic and reversed cyclic loading in PLA-based polymers. Thus, it is useful as material model input for numerical simulation purposes.
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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 | 0.000 |
| 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) | 0.003 | 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".