The 2018 Midwest Instruction and Computing Symposium
The Department of Computer Science at the University of North Dakota (UND) has been evaluating optical/imaging methods for measuring the quality of 3D printed parts. In particular, we are interested in optical/imaging methods that can detect and measure such quality issues as layer shifting, layer separation and splitting, overheating, dimensional accuracy, and infill errors. This paper describes our work towards the analysis of infill errors as the quality of the infill does impact the structural integrity of the part being made. Externally, a part may look acceptable, but if the infill is faulty the part may be structurally unsound. Furthermore, once a part is finished printing it is usually not possible to see the infill. Therefore, monitoring of the infill must be done while the part is being printed.
First published in the MICS 2018.
Tyler Welander, Ronald Marsh, and Md Nurul Amin. "G-code Modeling for 3D Printer Quality Assessment" (2018). Computer Science Faculty Publications. 22.