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CAD Ceramic Texture Synthesis Method Based on Convolutional Neural Networks.

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Názov: CAD Ceramic Texture Synthesis Method Based on Convolutional Neural Networks.
Autori: Yunyun Qiao1 qiaoyunyun@jcu.edu.cn, Kun Zhang2 zhangkun@jcu.edu.cn
Zdroj: Computer-Aided Design & Applications. 2025 Special Issue, Vol. 22, p60-73. 14p.
Predmety: *COMPUTER systems, *CERAMIC industries, CONVOLUTIONAL neural networks, COMPUTER engineering, DEEP learning, COMPUTER graphics
Abstrakt: Ceramic texture design is an essential key mechanism in current design and production plans. Traditional texture processing methods have certain limitations in design innovation. Therefore, this article uses neural networks to optimize the design of ceramic textures by recommending solutions. This paper evaluates the ceramic texture information on different nodes by designing and synthesizing textures using recursive neural networks. Through the application of computer graphics design assistance systems, it has been found that systematic methods for ceramic textures have certain differences in processing time. Therefore, in the time calculation system of computers, the optimization performance of ceramic nodes has a high accuracy, reaching 15%. In addition, in the process of ceramic CAD production, the results of this article not only have strong functionality in the productivity of CNN (Convolutional Neural Networks) ceramic synthesis but also play a certain reference role in the ceramic industry. [ABSTRACT FROM AUTHOR]
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Abstrakt:Ceramic texture design is an essential key mechanism in current design and production plans. Traditional texture processing methods have certain limitations in design innovation. Therefore, this article uses neural networks to optimize the design of ceramic textures by recommending solutions. This paper evaluates the ceramic texture information on different nodes by designing and synthesizing textures using recursive neural networks. Through the application of computer graphics design assistance systems, it has been found that systematic methods for ceramic textures have certain differences in processing time. Therefore, in the time calculation system of computers, the optimization performance of ceramic nodes has a high accuracy, reaching 15%. In addition, in the process of ceramic CAD production, the results of this article not only have strong functionality in the productivity of CNN (Convolutional Neural Networks) ceramic synthesis but also play a certain reference role in the ceramic industry. [ABSTRACT FROM AUTHOR]
ISSN:16864360
DOI:10.14733/cadaps.2025.S1.60-73