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Design, Fabrication, and Optimization of 3D Printed Pressure Sensors

Year: 2023

Presenter Name: Derrick Wong

Total joint arthroplasty (TJA) remains as the only viable option for many arthritis patients no longer responding to non-surgical treatment. However, failure rates for TJA joint implants as reported by the American Association of Hip and Knee Surgeons have been found to be 5-10% 10 years post-operatively. Common factors for failure include fracturing, implant instability, and biocompatibility concerns. However, there is currently no viable solution for implantable devices that allows for monitoring in vivo. Here we show that wireless pressure sensors can be fabricated using additive manufacturing (3D printing), and that printed sensors yield a measurable signal in frequency space that may be effectively characterized for future application with implantable devices. To perform this project, we designed sensors using SOLIDWORKs, simulated them to better understand their characteristics, and measured successful prints using a network analyzer for comparison with simulated values. In the span of this project, we performed five design cycles to iteratively optimize the sensor design. Each design involved adjustments to the inherent geometry of the previous design, in which we changed the intrinsic sensor properties to improve sensitivity and the observed frequency range. The findings from our design optimizations provide significant insight into improving the sensing ability of 3D printed pressure sensors, while also serving as a foundation for a finalized design that may be printed and further tested. We anticipate that the integration of 3D printed wireless pressure sensors has potential for future use in internal monitoring and improving clinical outcomes. The use of additive manufacturing for pressure sensors offers significant flexibility in fabrication and design geometry, which may be greatly useful in various biomedical device settings such as the previously mentioned implantable devices for joints.
University / Institution: University of Utah
Type: Poster
Format: In Person
Presentation #D91
SESSION D (3:30-5:00PM)
Area of Research: Engineering
Faculty Mentor: Yong Lin Kong