[MedJaw - Capstone project]

Dr. Benjamin Massenburg is a plastic surgeon working in pediatrics with a specialization in craniofacial surgery (surgery aimed to correct deformities in the jaw, facial, and neck structures). Noticing that many jaw physiotherapy devices are expensive and not designed for children, he aims to create a parametric CAD model for 3D printing a jaw therapy device which would increase accessibility to therapy for ankylosis, hardened fibrous tissue in the temporomandibular joint which leads to a condition called trismus. For children in particular, this would decrease the risk of choking or aversion to eating by increasing jaw mobility.

Be fully 3d printed and avaliable to patients and providers across the globe
withstand 100,000 cycles, or have easily replaceable components (think rubber bands and braces)
survive daily use wear and tear
be food safe and not having any choking hazards (Trismus patients especially at risk of choking)

This forum will mainly document my own progress and contributions. This is a team project, our official site can be found here

After getting the problem statement, I took initiative to come up with 4 seperate components that work together in a simple system. Bad sketch made using ms paint. The reason for the 4 components is due to each group member needing to eventually submit an analysis by the end of the quarter. Another group member took initive to come up with a component system, but we decide to use mine due to being more simple and closely following the industry standard device.

First presentation and initial housing design. Made in solidworks. I tried to have space for all the other components, while having a small and lightweight footprint. I originally did not optimize the design for 3d printing, which would be later adressed. Full group presentation here

In order to get some numbers and definitively get a result, I used a tension meter (in compression mode) and broke a bunch of V2 prototypes of varying wall thicknesses, infill types, and layer orintations.

First order of business was to print some oreintations and find the stress concentrations. I could run a bunch of simulations, but a couple hours to print a prototype and try my best to snapping it was much more "educational" as well as fun. Straigtned out some edges to reduce the need for supports, as well as finding the best printing orientation for strength.

Fabricating all the "victims" using Bambu Lab A1. The slider rails were also changed to be modular to adress the upcoming friction issue. I found that the strongest single part orientation would have the slider going over each layer ridge on the rail. More information in next section

One of the drawbacks of the drop test was the lack of numerical data, and the vagueness of the results. I can't sit there and drop the device 100s of times, which could reprent to wear and tear. I set out to come up with a way to simulate a drop, using the 3d printed geometry instead of the cad. I vibe-coded a script using Claude AI and many iterations that took the G-code from the slicer, placing voxels along the tool path, and then converting the Voxels to a mesh.

Originally I took the mesh that the python code spit out to run directly in Ansys Explicit Dynamics, but the mesh size had to exceed 1.7 million in order to accurately capture the layer lines and internal geometry. The solution was to once again consult my good friend Claude AI to write an Ansys LS Dyna keyword input file. After hours of debugging code and LS Dyna setup, I was able to sucessfully simulate an angled drop from 4 ft onto a steel plate, comparing a Gyroid Infill to a Rectilinear infill structure.

The final result led me to believe that Rectilinear would be slightly better in a drop. During physical testing, Gyroid performed better overall in applied pressure loads, So considering the improvement was 4.29% Gyroid was selected to be the printing infill that users would be instructed to later use. The anisentropic properties of 3d printing was not represented in the simulation, but will be later implemented to try to simulate more components. If a simulation workflow can be applied to all the components, we may also be able to adress the fatigue life!

Next, in order to adress the sliding friction, I designed a 3d printed test rig. From limited research on the jaw resistive force, I had a hanging mass represent that force with an arbitrary weight. We would measuring the friction indirectly, by finding the the effort (tension meter (N)) to get the slider to move from a rest (overcome static friction and resistive force). With a modular rail, I could compare the friction between 3 rail configurations using the Effort/Resist ratio. This test bench was also used to optimize the 3 different slider configurations designed by a teammate, totalling 9 unique system configurations.

Different configurations I tested

The input force and resistive force were measured by tension meter connected to matlab. The weight was water in jug.

The final result was a 65% increase in performance over the baseline design. Full team risk reduction presentation here

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