Effect of Thermal and Vibration Changes on Automated External Defibrillator Circuit Boards: Finite Element Analysis Study
Bibliographic record
Abstract
Background: An automated external defibrillator (AED) is a device that is used to prevent sudden death by delivering an electrical shock to restore the heart rhythm when experiencing cardiac arrest. Objective: This study was performed to analyze the vibration and thermal changes experienced by an AED medical device when exposed to shocks caused by patients' reactions, vibrations from mobile and air ambulances, and heat changes due to the battery component on the circuit board. Methods: Basically, AED is made from plastic, with the external parts containing the display, buttons, pad socket, and speaker, while the internal part entails the circuit boards comprising components such as resistors, capacitors, inductors, and integrated circuits, among others. In this study, the AED was modeled with the Ansys Workbench 2020 and calibrated based on static and dynamic loading to verify the static displacement and determine the first set of five frequencies obtained based on the unprestressed conditions. Results: Using the prestressed analysis with modifications, the next set of frequencies was obtained with an error margin of 0.0003% between each frequency. The modeled circuit board was used to examine the vibration and dynamic analysis for the rigid board. Similarly, thermal analysis was conducted on the modeled circuit board with the battery serving as the heat source. The rate of dissipation of heat around the board and its effect on the circuit components was evaluated. Conclusions: The modeled circuit board was reinforced with more support structures to mitigate the deformation effect. The deformation peaked at 33.172 mm, with minimum deformation at the edges of the board. Components with greater height, such as capacitors, experienced more pronounced deformation. Therefore, it is suggested that flat capacitors of lesser height would be suitable for future designs. Additionally, significant heat dissipation from the battery suggests a need for better dissipation pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".