Style transfer for CNC machine input data preprocessing
Advances in deep neural networks have led to impressive results in recent years. The new technologies such as cross-domain adaptation, reinforcement learning and generative adversarial networks have shown a real promise for industrial and real-life applications. In this paper, the results of the exp...
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| Main Authors: | , , , |
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| Format: | Статья |
| Language: | English |
| Published: |
Institute of Physics Publishing
2019
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| Subjects: | |
| Online Access: | https://www.scopus.com/record/display.uri?eid=2-s2.0-85076353555&origin=resultslist&sort=plf-f&src=s&st1=Style+Transfer+for+CNC+Machine+Input+Data+Preprocessing&st2=&sid=d26f2c404dd20f2650d6d115a369a105&sot=b&sdt=b&sl=70&s=TITLE-ABS-KEY%28Style+Transfer+for+CNC+Machine+Input+Data+Preprocessing%29&relpos=0&citeCnt=0&searchTerm= https://dspace.ncfu.ru/handle/20.500.12258/10144 |
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| Summary: | Advances in deep neural networks have led to impressive results in recent years. The new technologies such as cross-domain adaptation, reinforcement learning and generative adversarial networks have shown a real promise for industrial and real-life applications. In this paper, the results of the experimental research on designing, training and implementation of the preprocessing algorithm for the computer numerical control machine input were presented. The algorithm of neural network transfer of artistic style has demonstrated wide possibilities in the field of generating graphic content. This paper demonstrates the possibility of using a generating neural network for the synthesis of stylized images that can be used as input images for a computer numerical control machine. Thus, the proposed algorithm is pre-processing the input image. The design feature of the laser engraver does not allow styling using an arbitrary style image, so dotted or linearized binary images are used as a style. The proposed preprocessing algorithm allows synthesizing binary images reproduced by a laser engraver. At the same time, image generation is performed in one forward pass of the generating neural network |
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