Researchers Thomas Campbell and James F.X. Jones, both of the School of Medicine, University College Dublin, Ireland, have a created a new 3D printer for the medical field, detailing their work in the recently published ‘Design and implementation of a low cost, modular, adaptable and open-source XYZ positioning system for neurophysiology.’
The authors have created an open-source system that can be customized for a wide range of projects, relying on an XYZ positioning system capable of moving a sensor or probe. Like a gantry crane, this new FDM printer is run by a standard Raspberry Pi 3, Arduino Mega, RAMPS 1.4 motor shield, and NEMA17 bipolar stepper motors. The frame consists of 20×20 mm aluminum extrusion made with 3D printed parts, bolted together by brackets. ‘Entry cost’ for such a 3D printer was calculated at approximately $670.20.
With the integration of the Raspberry Pi 3, the authors were also able to incorporate the Open Computer Vision Library (OpenCV) stating that feature is what makes the system unique in comparison to other XYZ positioning systems. The open-source machine learning software library is used with automated movement, and the creators expect it to transform the exploration of mechanotransduction, the method for sensory neurons to change a mechanical stimulus to an electrical signal.
Movement of the 3D printer is controlled by the Arduino Mega, which in turn is controlled by the Raspberry Pi 3:
“Arranging the microcontrollers in this master-slave configuration permits the automation of complex movement paradigms through the Python3 programming language. The power source for the system depends on the intended use case. For neurophysiology a linear regulated 12 V DC power supply must be used to ensure low EMI how-ever for other applications a 12 V DC switching power supply suffices.”
Campbell and Jones chose PLA for the materials to print components, using a Prusa i3 MK3, modeling the calibration cube in Autodesk Fusion360, and stating that dimensions for each cube were measured with digital calipers six times. Supports were not necessary for any of the fabricated parts, all of which were designed with minimal overhang.
Build instructions include:
- Y-axis carriage assembly
- X and Z axes assembly
- Axis alignment
- Electronics and wiring
- Preparation of and uploading of Marlin firmware
- Setup of the Raspberry Pi 3 & OpenCV
- Creation of a terminal based operating system
For use in functional neurophysiology applications, the authors tested the machine to see if it was capable of prompting mechanotransduction within the muscle spindle. Activation thresholds were successfully shown for:
- Stretch distance
- Stretch velocity
- Stretch acceleration
“The main limitations of the XYZ positioning system are mechanical in nature,” concluded the authors. “In our implementation, the X & Z axis assembly is tall and heavy and as such we opted to reduce the Y and Z axis travel speeds to 2 mms x 1and 5 mms x 1respectively. This reduction in speed preserves positional integrity of the system by reducing the likelihood of stepper motors stepping erroneously. However, the assembly can be adjusted to the desired specific use case and a simple reduction the size of the Z-axis would greatly reduce its inertia and permit positional accuracy at greater travel speeds.
“All components and software utilized were open-source, free to access or available at low cost. Given the ease with which these components can be accessed and the potential that such a system offers, it is believed that other research groups may find this system an attractive and useful experimental tool.”
3D printers are being used—and created—for many purposes in medical applications like dental, bioprinting, also offering a wide range of tools for doctors and surgeons like medical models and instruments. What do you think of this news? Let us know your thoughts! Join the discussion of this and other 3D printing topics at 3DPrintBoard.com.[Source / Images: ‘Design and implementation of a low cost, modular, adaptable and open-source XYZ positioning system for neurophysiology’]
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