Design of Automatic Production Algorithm for Ceramic Patterns Based on Machine Learning

With the growth of high technology, the digitization of art and cultural relics is no longer a problem. At present, ceramic enterprises are gradually evolving from large-scale customization to personalized customization, and the demand for upgrading the intelligent manufacturing industry is increasing. A personalized ceramic pattern generation algorithm is on the verge of emergence. With the theoretical growth of neural networks and the improvement of hardware computing power, machine learning (ML) and deep learning (DL) have achieved remarkable achievements in multiple fields. Image feature extraction technology and automatic generation technology have both received great attention from the academic community. Generative Adversarial Networks (GANs) have promoted research applications in image generation and restoration, text generation, video prediction, and style transfer, demonstrating their enormous potential in image processing. This article designs an ML based algorithm for automated production of ceramic patterns. By using ML technology, useful features are automatically extracted from a large amount of ceramic pattern data, and these features are used to train the model, thereby generating new ceramic patterns. The experimental results show that the algorithm can be well applied to practical cases and effectively achieve automated production of ceramic patterns.