Industry 5.0 as integration of the internet of knowledge and the internet of things
Автор: Evgenev G.B.
Журнал: Онтология проектирования @ontology-of-designing
Рубрика: Общие вопросы формализации проектирования: онтологические аспекты
Статья в выпуске: 1 (31) т.9, 2019 года.
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The methodology of creating systems of "Industry 5.0" class with the use of artificial intelligence technologies is proposed. The methodology is based on multi-agent methods of creating knowledge bases and is suitable for the development of design and control systems for digital intelligent industries. An Integrated structure of the Internet of Knowledge and the Internet of Things has been developed. The life cycle of mechanical engineering products is analyzed and the methods of application of the Internet of Knowledge and the Internet of Things at various stages of this cycle are proposed. The functional decomposition of the main stages of the life cycle is given. The conceptual foundations of the Internet of Knowledge are given. Multi-agent methods of knowledge base creation have been developed. The meta-ontology of engineering agents is proposed. The principles of construction of multi-agent systems for semi-automatic design of products are described. The description of the capabilities of intelligent programming of processing systems for CNC machining in terms of the formation of machining trajectory and transition areas is given. The possibilities of intelligent systems of design and regulation of technological processes are described. It is proposed to use the IDEF3 standard to create metamodels of technological processes and modified route maps for the formation of knowledge bases. The description of the intellectual system of operative management of industrial production is given. The General functional model of operation of an industrial enterprise operation is considered. The functional blocks of the system are described.
Industry 4.0, industry 5.0, digital manufacturing, internet of knowledge, internet of things, intelligent systems
Короткий адрес: https://sciup.org/170178814
IDR: 170178814 | DOI: 10.18287/2223-9537-2019-9-1-7-23