Crack patterns corresponding to the residual strength plateau of ceramics subjected to thermal shock

The formation strength plateau of ceramics is addressed. A set of of 99A1203 are conducted, mechanism of the residual subjected to thermal shock thermal shock experiments where the thin specimens of 1 mm× 10 mm×50 mm exhibit parallel through edge cracks, and thus permit quantitative measurements of the crack patterns. The cracks evolve with the severity of ther- mal shock. It is found that there is a correlation between the length and density of the thermal shock cracks. The increase of crack length weakens the residual strength, whereas the increase of crack density improves it. In a considerably wide temperature range, the two contrary effects just counteract each other; consequently a plateau appears in the variation curve of the residual strength. A comparison between the numerical and experimental results of the residual strength is made, and they are found in good agreement. This work is helpful to a deep understanding of the thermal shock failure of ceramics.

A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

Ceramic tiles are one of the most indispensable materials for interior decoration.The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures.In this paper,we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network(GAN).The generated tile images can be tailored to meet the specific needs of the user for the tile textures.The proposed method consists of four steps.Firstly,a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN.Secondly,for each ceramic tile image in the dataset,the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated sketches.Thirdly,the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training speed.Finally,we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of“one style with multiple faces”.We conduct the training process of the proposed generation network on 2583 ceramic tile images dataset.To measure the generative diversity and quality,we use Frechet Inception Distance(FID)and Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)metrics.The experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images,with FID of 32.47 and BRISQUE of 28.44.