Fabrication and tensile testing of 3D printed continuous wire polymer composites
Bibliographic record
Abstract
Purpose This paper aims to evaluate and predict the tensile properties of additively manufactured continuous wire polymer composites (CWPCs). Design/methodology/approach An open-source 3D printer was modified to print CWPCs where metal wires act as a reinforcement within a polymer matrix. The influence of different wire materials and diameters on the tensile modulus and ultimate tensile strength was studied. Different polymer matrixes were used to investigate the effect of the matrix on CWPCs’ tensile properties. The behaviour of samples was predicted analytically using the rule of mixture micromechanical approach and investigated experimentally using an American society for testing and materials standard tensile test. Findings Experimental results showed improvement in the elastic modulus and ultimate strength of CWPCs compared with non-reinforced specimens. Deviation between the experimental data and the analytical prediction was found to be dependent on the matrix type, wire volume fraction and wire material. Originality/value This paper introduces novel continuous metal wire-reinforced 3D printed composites. The continuous wire inside the print can be used as a strain gauge which can give an early alert for material failure. Applications for CWPCs include 3D-printed pressure and temperature sensors which measure the change in the wire’s electrical resistance and 3D-printed heaters which would work by supplying current through continuous wires.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".