Shape Memory Properties of Additive Manufactured Continuous Metallic Wire-Reinforced PLA with Electrothermal Activation
Bibliographic record
Abstract
In this study, additive manufacturing of reinforced parts using metallic wire as both a reinforcement component and a shape memory stimulus through Fused Deposition Modeling (FDM) was investigated. A Shape Memory Polymer (SMP) restores its original shape and recovers it upon specific stimuli. This research employed chromium-nickel metal wire as a reinforcing component to enhance mechanical properties and introduce the capability for thermal stimulation of polylactic acid (PLA) via electrical current using the "in-situ impregnation" method within FDM process. Reinforced specimens were fabricated with wire with two diameters of 0.1 and 0.15 mm, along with two volume percentages of 5 and 10. Comprehensive evaluations encompassing mechanical (tensile and flexural) and thermal properties of the printed specimens were conducted. The outcomes revealed a significant enhancement in both tensile and flexural properties of the polymer matrix due to the embedding metallic wire, even under elevated temperatures during bending test. Furthermore, the thermal properties of the reinforced specimens were examined by subjecting them to various voltages, resulting in temperature ranging from 36.4 to 150.1°C. These findings highlight the ability to tailor a wide range of mechanical properties and shape recovery in the reinforced specimens by carefully selecting the wire volume fraction, voltage, and wire diameter, thus regulating the materials properties with specific application requirements.
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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) | 0.001 | 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".