Additive manufacturing of shape memory polymers: effects of print orientation and infill percentage on mechanical properties
Bibliographic record
Abstract
Purpose Material extrusion additive manufacturing, also known as fused deposition modeling, is a manufacturing technique in which objects are built by depositing molten materials layer-by-layer through a nozzle. The use and application of this technique has risen dramatically over the past decade. This paper aims to first, report on the production and characterization of a shape memory polymer material filament that was manufactured to print shape memory polymer objects using material extrusion additive manufacturing. Additionally, it aims to investigate and outline the effects of major printing parameters, such as print orientation and infill percentage, on the elastic and mechanical properties of printed shape memory polymer samples. Design/methodology/approach Infill percentage was tested at three levels, 50, 75 and 100 per cent, while print orientation was tested at four different angles with respect to the longitudinal axis of the specimens at 0°, 30°, 60° and 90°. The properties examined were elastic modulus, ultimate tensile strength and maximum strain. Findings Results showed that print angle and infill percentage do have a significant impact on the manufactured test samples. Originality/value Findings can significantly influence the tailored design and manufacturing of smart structures using shape memory polymer and material extrusion additive manufacturing.
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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) | 0.001 | 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".