NEURAL NETWORKS FOR TEXTURE SIMULATION IN PRINTS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6632Keywords:
Neural Networks, Texture Synthesis, Printing Technology, Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Material VisualizationAbstract [English]
Computer images, material science and additive manufacturing are all important fields of study, which work together to study texture simulation in printed materials. The fine-grained reality and variety of real-world materials is difficult to represent using traditional texture creation methods based on procedural algorithms and physical modelling. This research explores the use of neural networks to simulate the textures in prints. The main focus of the book is convolutional neural networks (CNNs) and generative adversarial networks (GANs). The suggested method attempts to rightfully recreate the feel and look of textures in order to make digital and physical results look and feel more like the real thing. The study begins with the collection of structured data about various textures of prints. Next cleaning techniques such as normalisation, patch extraction and addition are applied to improve generalisation of the model. For extracting hierarchy features, CNN based model is used. A GAN design generates new images by learning the hidden patterns of the surfaces of materials. To locate the right combination between image sharpness and reality, training methods have loss function tuning, flexible learning rate plans, and aggressive optimisation. This system can be used for 3D printing, digital manufacture and virtual modelling, all of which benefit from being able to represent the correct materials for better results in terms of looks as well as functionality.
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Copyright (c) 2025 Manish Chaudhary, Dr. Taranath N L, Mona Sharma, Ashu Katyal, Dr. Shashikant Patil, Jaskirat Singh
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