Abstract
Digital production dictates new approaches to the organization of technological processes, including the development of cyber-physical systems within the framework of Industry 4.0. The development of these systems involves the use of not only classical methods, but also additive technologies in production. The article deals with the concept of a smart production system to find the optimal technological process, which is based on the user defined constraints and expert data of the cloud cyber-physical system.
Highlights
Today, by combining achievements in digital technologies such as wireless networks, cloud computing, big data and artificial intelligence on the one hand, and progress in smart materials, nanotechnology and 3D printing on the other, we are around of the beginning of the Fourth industrial revolution
Universal access to the global network and a huge amount of user data open up unlimited opportunities that people could not imagine a few years ago [1]
Increased computing power, augmented reality and virtual reality systems will be critical in implementation of many smart manufacturing strategies
Summary
By combining achievements in digital technologies such as wireless networks, cloud computing, big data and artificial intelligence on the one hand, and progress in smart materials, nanotechnology and 3D printing on the other, we are around of the beginning of the Fourth industrial revolution. Increased computing power, augmented reality and virtual reality systems will be critical in implementation of many smart manufacturing strategies. Integration of these technologies into smart factories is necessary for rapid adaptation of technological innovations in order to produce high-quality products and services. The implementation of the concept "industry 4.0" provides for the formation of cyberphysical systems (Cyber-Physical Systems — CPS) Such systems combine hardware, process equipment and logistics systems [3]. Process selection can be made based on material selection, part size and construction quality [5] This difference in process selection criteria makes it difficult to create and integrate a universal logic distributor into a dynamic and autonomous CPS. Since the number of processes and process combinations in CPS is limited, programming in constraints is ideal for handling the task
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