Advances in Endovascular Intervention Using Biomaterials: Study on Heat Efficiency of Scissor-Type Ultrasonic Catheter Device

Morita, Minoru; Yang, Jingjing; Jiang, Zhongwei

doi:10.1155/2021/5543520

Cited by 4 publications

(1 citation statement)

References 14 publications

(13 reference statements)

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“…For example, the color of produced anatomical structures manufactured by color 3D printing should be as close as possible to the real anatomical structures [1] . It is important to make a precise prediction for the color reproduction of the printed anatomical structures which can enhance the accurate planning of surgery and increase its efficacy in medical education and clinical practice [2], [3] . Just like color reproduction in 2D or 2.5D printing, color prediction model has the similar importance to control color reproduction for color 3D printing, which facilitates to control the relationship between input and output colors to improve the color reproduction quality.…”

Section: Introductionmentioning

confidence: 99%

Research on color prediction model based on radial basis function neural network for 3D printing

shao,

Zhang,

Wang

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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3D printing technology has enabled full-color printing. Research on factors and controls affecting color reproduction in full-color 3D printing has attracted more and more attention. Among them, the background factor and the finish type are two key factors which affect the color reproduction of 3D printing. In this paper, the Poly jet J750 was selected as the output device and one color prediction model based on RBF (Radial Basis Function) neural network was proposed to predict color reproduction for color 3D printing. It contains three input variables, such as the original chromatic coordinates of the digital files, i.e., CIE1976L*a*b*, the finish type of the printed color blocks, and the background factor in the proposed model. The measured color chromatic coordinates of the printed color blocks were set as output variables for the model. The prediction performance of the model was evaluated by using the mean absolute error (MAE), the coefficient of determination (R 2 ) and the CIEDE2000 color difference. It shows that the proposed model had a good prediction effect. In addition, it also shows that the white background has a better effect on the color prediction than those of the black and neutral gray background. And for the finish type, the Glossy has a better effect on the color prediction than that of the Matte.

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Section: Introductionmentioning

confidence: 99%