Additively manufactured substrates for vibration-based piezoelectric energy harvesting: design, fabrication and experimental validation
Bibliographic record
Abstract
Abstract Piezoelectric (PZT) energy harvesting technologies have regained popularity due to the increasing global demand for renewable electricity capacity and the sustainability requirements to be met by 2028. The evolution of this technology involves exploring various geometries to meet the natural frequency and power spectral density requirements. The rise of additive manufacturing has unlocked new possibilities for producing more complex geometries to further meet these requirements. This research focuses on the design, fabrication, and testing of a low-frequency, PZT-based vibration energy harvesting unit that employs additively manufactured substrates. The proposed unit is biologically inspired by the geometry of a golden tortoise beetle wing, known for its high flexibility, strength, and robust protection. The design features a rim structure divided into six equally sized sections, each with a PZT unit shaped in a meandering pattern made of beams with nonuniform thicknesses. The symmetry of each unit prevents charge cancellation caused by torsional effects. The substrate was 3D printed using the laser powder bed fusion technique. A two-step heat treatment process was employed to enhance the substrate’s mechanical properties, such as yield strength. The PZT material was fabricated using dicing techniques and bonded to the substrate using electrically conductive epoxy. In addition to the conducted experiments to obtain the power spectrum for excitations at the fundamental natural frequency, the harvester was modeled using COMSOL software to obtain the natural frequency and power plots. The model and test results were in good agreement and the power density demonstrates its excellence compared to notable similar works in the literature.
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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) | 0.001 | 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".