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Ireland: Researchers Create Open-Source 3D Printer for Neurophysiology

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.

Wiring of XYZ System. (A) RAMPS 1.4 shield (top) and Arduino Mega (bottom). (B) RAMPS 1.4 shield and microstepping jumpers (top). RAMPS 1.4shield with microstepping jumper pins installed (bottom). Note, to enable 1/16 microstepping for each stepper motor, it is necessary to install three jumpers per motor as encircled. (C) A4988 stepper motor drivers shown individually (top) and installed on RAMPS 1.4 shield (bottom). (D) Connecting the LCD screen to the RAMPS 1.4 shield. First, the smart adapter module is seated on the pins at the end of the RAMPS 1.4 shield. Next, EXP1 and EXP2 on the smart module should be connected to their corresponding ports on the reverse of the LCD screen. (E) The Arduino Mega and Raspberry Pi 3 can be connected over USB using a type A male to type B male connector. (F) Wiring of limit switches and stepper motors to RAMPS 1.4 shield. Note both the color orientation for stepper motor wiring and the highlighted pins for limit switch wiring.10T. Campbell, J.F.X. Jones /HardwareX 7 (2020) e00098

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

Stretching the muscle spindle to study mechanotransduction. (A) Afferent nerve activity from a stretched muscle spindle. Brief pulses of stretch wereapplied to the lumbrical every two seconds in order to elicit mechanotransduction from the muscle spindle. Each Stimulus pulse indicates the initiation of a stretch. Filtered nerve activity is represented in blue, unfiltered in green. (B) Mechanotransduction activation thresholds were assessed with gradual increments in the stretch distance, speed or acceleration. For this filtered unit, activation thresholds were observed at 14.0 mms x 1and 50 mms x 2. Increased stretch distance, speed or acceleration are associated with increased nerve activity (Filtered Spike Rate). (C) Overdraw of filtered nerve activity observed in(B) indicates that this was a single-unit recording. All data was recorded in Spike2 (Cambridge Electronic Design). ENG, Electroneurogram. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)18T. Campbell, J.F.X. Jones /HardwareX 7 (2020) e00098.

“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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I-Form and Gallomanor launch the ‘3D Printing a Sustainable World’ competition

I-Form, a Science Foundation Ireland (SFI) Research Centre for Advanced Manufacturing, and Gallomanor, an online science platform, have launched a 3D printing competition. Dubbed “3D Printing a Sustainable World”, this competition aims to ‘Shape the Future’ through environmentally-friendly ideas which replace conventionally made parts, pieces of art, or energy-based processes. The winning idea will be […]
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Students Learn Digital Manufacturing Through Design and 3D Printing of Turbine Blades

In a paper entitled “Application of Additive Manufacturing in Design & Manufacturing Engineering Education,” a pair of researchers from University College Dublin detail how they implemented a program on digital manufacturing and materials processing using 3D printing in an undergraduate engineering course. The students used 3D printing technology to fabricate a turbocharger turbine part. Three research questions were presented:

  • Can the use of digital manufacturing in engineering education increase student engagement?
  • Can the use of self-guided learning via digital manufacturing increase insights and understanding of the design and manufacturing process?
  • Can the use of self-guided learning increase the enjoyment and desire to learn?

The study involved a class of 90 undergraduate engineering students. Introductory lectures were given on topics such as digital manufacturing, additive and subtractive manufacturing, and 3D design and printing processes. The students were given background information along with examples of publications on turbine design, then were divided into groups of three and given periods between four and seven weeks to design and test turbocharger blades.

“As the practical element of the course was carried out over a 4-week period, there was considerable potential for competition between the groups as well as for peer learning,” the researchers state. “This helped to facilitate multiple learning styles and environments. From a manufacturing viewpoint there is the initial challenge of understanding why and where to use certain processes.”

The students were given a lot of freedom, as no prescribed methodologies or solutions on turbine design were provided. The project was designed to be carried out for low cost; two 3D printers were used, one of them a Zmorph. The material used was PLA. Cura slicing software was used, along with Autodesk Inventor Professional for design. Four major components were included in the turbine design: the turbine itself, the housing, the turbine shaft and the mounting unit. The students had to consider the following parameters: blade radius, blade angle, blade thickness, and number of blades.

The 3D printing itself had to be completed within a 40 minute period, and the turbine performance and characterization had to be completed within an hour and a half prototyping lab. Each student group had to determine what printing settings to use. Once the part was completed, the turbine speed, dimensions, and layer morphology were evaluated, followed by a feedback session.

A student survey was carried out to evaluate the students’ prior knowledge in 3D printing as well as the level of interest and value in the course. All of the students had some prior knowledge of 3D design, but limited experience in 3D printing. The researchers conclude that in the future, it may be useful to offer different levels of challenge to the students based on their prior experience.

Overall, the course was highly successful, with the students reporting largely positive and enthusiastic feedback. The researchers state that the course could have benefited from more than one prototyping session, which may be included in a future course. The benefits of digital manufacturing and 3D printing were clearly shown, however.

“The course has great potential as a platform learning experience to educate engineers in a number of critical areas of digital manufacturing, covering innovation, engineering design, manufacturing, simulation, and prototyping whilst being low cost and easily replicated,” the researchers conclude.

Authors of the paper include Dr. Shane G. Keaveney and Professor Denis P. Dowling.

Discuss this and other 3D printing topics at 3DPrintBoard.com or share your thoughts below. 

 

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UCD opens $25.7 million advanced manufacturing research center with 3D printing focus

The I-Form Advanced Manufacturing Research Center for 3D printing and digital technologies has been opened at University College Dublin (UCD), Ireland. The facility has been created at a cost of €22.2 million ($25.7 million) provided by the government-backed Science Foundation Ireland (SFI) and industry stakeholders. Commenting on the launch Heather Humphreys, Ireland’s Minister for Business, Enterprise and Innovation, said, “Innovation […]