Additive manufacturing of shape memory polymers: effects of print orientation and infill percentage on shape memory recovery properties
Bibliographic record
Abstract
Purpose The purpose of this paper is to study the shape memory properties of SMP samples produced through a MEAM process. Fused deposition modeling or, as it will be referred to in this paper, material extrusion additive manufacturing (MEAM) is a technique in which polymeric materials are extruded though a nozzle creating parts via accumulation and joining of different layers. These layers are fused together to build three-dimensional objects. Shape memory polymers (SMP) are stimulus responsive materials, which have the ability to recover their pre-programmed form after being exposed to a large strain. To induce its shape memory recovery movement, an external stimulus such as heat needs to be applied. Design/methodology/approach This project investigates and characterizes the influence of print orientation and infill percentage on shape recovery properties. The analyzed shape recovery properties are shape recovery force, shape recovery speed and time elapsed before activation. To determine whether the analyzed factors produce a significant variation on shape recovery properties, t-tests were performed with a 95% confidence factor between each analyzed level. Findings Results proved that print angle and infill percentage do have a significant impact on recovery properties of the manufactured specimens. Originality/value The manufacturing of SMP objects through a MEAM process has a vast potential for different applications; however, the shape recovery properties of these objects need to be analyzed before any practical use can be developed. These have not been studied as a function of print parameters, which is the focus of this study.
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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".