Graduation Semester and Year
2018
Language
English
Document Type
Thesis
Degree Name
Master of Science in Mechanical Engineering
Department
Mechanical and Aerospace Engineering
First Advisor
Panayiotis S Shiakolas
Abstract
Commonly used additive manufacturing platforms have a single extrusion module based on Fused Filament Fabrication (FFF) and their processing software generates G-Codes for this FFF module using defined process parameters. These platforms and software do not accommodate different processing modules such as viscous extruders or Direct Ink Writing (DIW). This research is focused on the development of a Pneumatic Extrusion Module (PEM) capable of dispensing viscous materials such as gels or slurries controlled through a digital pneumatic valve. A PEM is developed, integrated and its performance is evaluated on a multi-modality additive manufacturing platform in the MARS Lab. The operation of PEM is controlled through an FPGA that communicates with the traditional G-Code for 3D printing in real-time. A methodology is developed for characterizing 3D printed strand width of a poly-urethane based photocurable resin based on process parameters, namely print speed and extrusion pressure using an Artificial Neural Network (ANN) model. During the additive manufacturing process, in real-time and as instructed from the G-code, the PEM control pressure is evaluated using another ANN model. Using this methodology and the developed hardware tools 3D constructs have been successfully fabricated. The results of this research show that PEMs module can be successfully and seamlessly integrated on a multi-modality platform for the fabrication of multi-material constructs using different processing.
Keywords
Machine learning, FPGA, Artificial neural networks, Additive manufacturing, 3D printing
Disciplines
Aerospace Engineering | Engineering | Mechanical Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Dhal, Kashish, "On the Development and Integration of Pneumatic Extrusion Module and a Methodology to Identify Process Parameters for Additive Manufacturing using Machine Learning" (2018). Mechanical and Aerospace Engineering Theses. 14.
https://mavmatrix.uta.edu/mechaerospace_theses/14
Comments
Degree granted by The University of Texas at Arlington