3D printed continuous wire polymer composites strain sensors for structural health monitoring
Bibliographic record
Abstract
Abstract This study presents the electromechanical properties of three-dimensional (3D) printed unidirectional continuous wire polymer composite (CWPC) to study the correlation of the elastic mechanical deformation and the electrical resistance under uniaxial loading conditions. Two kinds of wires were used for this study: copper (Cu) and nichrome (NiCr). 3D printing was utilized due to its flexibility in design and structure for different applications. From mechanical testing, the NiCr CWPCs demonstrated an increase of 13.5% and 54% in ultimate tensile strength and Young’s modulus, respectively, compared to pure 3D printed Poly(lactic acid) while the Cu CWPC did not exhibit significant improvement in the mechanical properties. A direct linear relationship was observed between the applied tensile strain and the measured electrical resistance for both Cu and NiCr CWPCs indicating the ability of these 3D printed structures to be used as a sensor to measure stress/strain in the real time. In addition, the sensitivity of both composites in terms of gauge factor, representing the relative change in the electrical resistance with the tensile strain of the material, were found to be 1.17 ± 0.06 and 1.13 ± 0.07 for Cu and NiCr CWPCs, respectively. This sensitivity was compared with a simple analytical model and showed a good agreement with the experimental results. Finally, the reliability of these CWPCs was evaluated by conducting a cyclic loading test within their elastic ranges. The results of this work will enable the manufacture of integrated sensors within 3D printed components with improved mechanical properties and increased functionality.
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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 | 0.001 | 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".