New carpet pattern design with deep learning
The fact that digitalization touches every aspect of today's world has often been realized with the ideas of the industry and projects carried out for the development of the industry. On the basis of many of the projects realized is located Artificial Intelligence technologies. While Artificial Intelligence shows its effective activities in many areas of the industry such as production, quality and planning, and it can also support designers in pre-production design. Within the scope of this study, it is aimed to produce new product designs by using the image data of the most demanded products obtained from a carpet manufacturing company with Deep Convolutional Generative Adversarial Networks, which is a special variant of the Generative Adversarial Networks architecture, which is one of the Artificial Intelligence-Deep Learning sub-architectures. Before performing the model training with the images obtained from the carpet manufacturer company, the image generation performances of a ready-made data set and two different Python libraries, Keras and PyTorch, were compared. As a result, it was determined that the performance of the PyTorch Python library in generating images was higher, and the model built with real images was repeated. The synthetic designs produced were presented to the carpet manufacturer, and it was aimed to bring the role of the designer to a position that guides the design models with designer's experience and knowledge in the design process, which is a complex and stochastic process before production.