Automated Print Quality Assessment Method for 3D Printing AI Data Construction


Hyun-Ju Yoo, Nammee Moon, Journal of Information Processing Systems Vol. 18, No. 2, pp. 223-234, Apr. 2022  

https://doi.org/10.3745/JIPS.04.0240
Keywords: Data construction, Dimensional Accuracy, Field-of-View (FOV), quality assessment, 3D Printing
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Abstract

The evaluation of the print quality of 3D printing has traditionally relied on manual work using dimensional measurements. However, the dimensional measurement method has an error value that depends on the person who measures it. Therefore, we propose the design of a new print quality measurement method that can be automatically measured using the field-of-view (FOV) model and the intersection over union (IoU) technique. First, the height information of the modeling is acquired from a camera; the output is measured by a sensor; and the images of the top and isometric views are acquired from the FOV model. The height information calculates the height ratio by calculating the percentage of modeling and output, and compares the 2D contour of the object on the image using the FOV model. The contour of the object is obtained from the image for 2D contour comparison and the IoU is calculated by comparing the areas of the contour regions. The accuracy of the automated measurement technique for determining, which derives the print quality value was calculated by averaging the IoU value corrected by the measurement error and the height ratio value.


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Cite this article
[APA Style]
Yoo, H. & Moon, N. (2022). Automated Print Quality Assessment Method for 3D Printing AI Data Construction. Journal of Information Processing Systems, 18(2), 223-234. DOI: 10.3745/JIPS.04.0240.

[IEEE Style]
H. Yoo and N. Moon, "Automated Print Quality Assessment Method for 3D Printing AI Data Construction," Journal of Information Processing Systems, vol. 18, no. 2, pp. 223-234, 2022. DOI: 10.3745/JIPS.04.0240.

[ACM Style]
Hyun-Ju Yoo and Nammee Moon. 2022. Automated Print Quality Assessment Method for 3D Printing AI Data Construction. Journal of Information Processing Systems, 18, 2, (2022), 223-234. DOI: 10.3745/JIPS.04.0240.