Abstract
The recently created device for printing images using mold spores has opened up newpossibilities for creating living images from digital ones. In this process, digital images
are converted into instructions for the Mold Printer. This thesis addresses the optimization of this process by focusing on three core aspects: algorithmic efficiency, parameter
tuning, and visualization techniques. The primary objective is to simplify and enhance
the generation of printer instructions from digital images to improve the quality and
efficiency of the final output.
To achieve this, we developed and implemented two algorithms designed to convert
digital raster images into printable G-Code instructions. Additionally, an algorithm
for optimizing the printer instructions was developed to minimize print times. These
algorithms were tested and evaluated to verify their performance and identify areas for
improvement. Key parameters for the conversion, such as the insertion depth of the
needle, were identified and iteratively optimized to enhance print quality.
Our findings indicate that the path optimization algorithm does reduce printing time
for all images, but not all images benefit equally. For some images, especially complex
ones, the reduction in printing time is significantly more pronounced. Additionally, the
integration of visualization tools proved very helpful in refining parameters, allowing
for real-time feedback. Challenges such as handling varying mold growth conditions and
minimizing print defects were addressed through iterative testing and refinements of the
printer. Finally, some limitations and potential ideas for future work are presented and
discussed.
| Date of Award | 2024 |
|---|---|
| Original language | English (American) |
| Supervisor | Volker Christian (Supervisor) |