Version 1
: Received: 4 April 2024 / Approved: 5 April 2024 / Online: 5 April 2024 (06:09:15 CEST)
How to cite:
Okamoto, T.; Ura, S. Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System. Preprints2024, 2024040409. https://doi.org/10.20944/preprints202404.0409.v1
Okamoto, T.; Ura, S. Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System. Preprints 2024, 2024040409. https://doi.org/10.20944/preprints202404.0409.v1
Okamoto, T.; Ura, S. Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System. Preprints2024, 2024040409. https://doi.org/10.20944/preprints202404.0409.v1
APA Style
Okamoto, T., & Ura, S. (2024). Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System. Preprints. https://doi.org/10.20944/preprints202404.0409.v1
Chicago/Turabian Style
Okamoto, T. and Sharifu Ura. 2024 "Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System" Preprints. https://doi.org/10.20944/preprints202404.0409.v1
Abstract
Verifying the accuracy of a 3D-printed object involves using an image processing system. This system compares images of the CAD model of the object to be printed with its 3D-printed counterparts to identify any discrepancies. It is important to note that the integrity of the accuracy measurement is heavily dependent on the image processing settings chosen. This study focuses on this issue by developing a customized image processing system. The system generates binary images of a given CAD model and its 3D-printed counterparts and then compares them pixel-by-pixel to determine the accuracy. Users can experiment with various image processing settings, such as grayscale to binary image conversion threshold, noise reduction parameters, masking parameters, and pixel-fineness adjustment parameters, to see how they affect accuracy. The study concludes that the grayscale to binary image conversion threshold has the most significant impact on accuracy and that the optimal threshold varies depending on the color of the 3D-printed object. The system can also effectively eliminate noise (filament marks) during image processing, ensuring accurate measurements. Additionally, the system can measure the accuracy of highly complex porous structures where the pores size, depth, and distribution are random. The insights gained from this study can be used to develop intelligent systems for the metrology of additive manufacturing.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
