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<p>With the development of AI technology, the development of the ceramic industry has ushered in a good opportunity. This paper discusses the application of AI technology in the field of ceramics on the basis of deep convolutional generative adversarial network, using Adam optimizer to further improve the performance of the model, using the hidden space to add noise to each layer of the network, and proposing a digital ceramic texture image generation model based on DCGAN. Meanwhile, the K-Means algorithm and feature fusion mechanism with BWP as the evaluation index are introduced to improve the Faster R-CNN algorithm and construct the ceramic defect detection model. The scores of each subjective evaluation index of image generation quality are greater than 4, which are located in the interval of higher evaluation. The scores of IS, BRISQUE, and NIQE are 3.39, 22.57, and 4.43, and the quality of ceramic texture image generation of this paper’s model is better than that of other algorithms. The ceramic defect detection model’s detection accuracy for all five ceramic defects is higher than 92.68%, with an 8.54% improvement over the original algorithm’s defect detection performance. The research in this paper has important theoretical and practical significance for ceramic product design, quality control, and troubleshooting of production equipment.</p>
</abstract>

With the development of AI technology, the development of the ceramic industry has ushered in a good opportunity. This paper discusses the application of AI technology in the field of ceramics on the basis of deep convolutional generative adversarial network, using Adam optimizer to further improve the performance of the model, using the hidden space to add noise to each layer of the network, and proposing a digital ceramic texture image generation model based on DCGAN. Meanwhile, the K-Means algorithm and feature fusion mechanism with BWP as the evaluation index are introduced to improve the Faster R-CNN algorithm and construct the ceramic defect detection model. The scores of each subjective evaluation index of image generation quality are greater than 4, which are located in the interval of higher evaluation. The scores of IS, BRISQUE, and NIQE are 3.39, 22.57, and 4.43, and the quality of ceramic texture image generation of this paper’s model is better than that of other algorithms. The ceramic defect detection model’s detection accuracy for all five ceramic defects is higher than 92.68%, with an 8.54% improvement over the original algorithm’s defect detection performance. The research in this paper has important theoretical and practical significance for ceramic product design, quality control, and troubleshooting of production equipment.

The Use of AI Technology in Digital Ceramic Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"The Use of AI Technology in Digital Ceramic Technology"}

With the development of AI technology, the development of the ceramic industry has ushered in a good opportunity. This paper discusses the application of AI...

The Use of AI Technology in Digital Ceramic Technology

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 18. März 2024

Akzeptiert: 06. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1503

Schlüsselwörter
DCGAN, Faster R-CNN, Image generation, Defect detection, Digital ceramics

© 2024 Huayan Zheng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Use of AI Technology in Digital Ceramic Technology

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 18. März 2024

Akzeptiert: 06. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1503

SchlüsselwörterDCGAN, Faster R-CNN, Image generation, Defect detection, Digital ceramics

© 2024 Huayan Zheng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
DCGAN, Faster R-CNN, Image generation, Defect detection, Digital ceramics