Abstract
Deep learning has been particularly successful in many fields such as computer vision in recent years. However, only few applications of Deep Learning can be found in the manufacturing context. Potentially overloading a computer network with the large amounts of data as well as limited computing power represent a big obstacle, especially for production sensitive data. To make Deep Learning applicable in production, these problems are described and a solution utilizing Time-Sensitive Networking Standards and transfer learning is developed. Then an exemplary application for the visual control of workpieces in ongoing production is implemented in a test factory.
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