# Core Ontology for Describing Production Equipment According to Intelligent Production
## 1 Introduction  
Industry 4.0 forms a new philosophy of production. Physical and virtual components are combined in cyber-physical systems [1], interacting with each other through the Internet of Things (IoT), Industrial IoT (IIoT), and cloud technologies. Reference study [2] performed analyses and made estimates regarding how integrating Internet of Things-based decision support systems along with production processes facilitates automatic data gathering and inspection of industrial plants networks in real time. Cyber-physical systems are the foundation of smart manufacturing. Smart manufacturing offers tremendous promise. It includes everything from collecting data from sensors to complex algorithms for analysis. One of its prospective components is the ability to predict: general production trends, anticipation of possible downtime and early detection and elimination of possible causes, early detection of possible equipment breakdowns and corresponding preventive maintenance. Corresponding software implementations will allow the performance of specified operations of any complexity within smart manufacturing in real time. Another basis for the creation and successful functioning of smart manufacturing is the digital twin. The digital twin is a reflection of physical objects andvirtual models [3]. Reference study [4] performed analyses and made estimates regarding the robust designs and performance of wireless sensor network-based smart systems. To date, there is no single developed approach to most of these concepts, and the relationship between them in the context of Industry 4.0, so there are a number of areas that are developing in parallel and sometimes there are certain inconsistencies. This applies both to methodological approaches and individual implementation developments, as well as to international industry standards. However, all of this does not have a significant impact on the overall direction of development. Reference study [5] used and replicated data from BCG, BDO, Cappgemini, Management Events, PAC, and PwC, and performed analyses and made estimates regarding production network performance. A new approach to the organization of production leads to the intellectualization of all its stages and the introduction of the latest methods for collecting, storing and processing industrial information. Research study [6] performed analyses and made estimates regarding intelligent remote equipment control and optimization of the manufacturing processes through autonomous robotic systems and predictive maintenance. The use of IIoT and cloud technologies makes it possible to obtain objective and accurate data on the state of equipment and production. Research study [7] performed analyses and made estimates regarding public acceptance of and intention to use self-driving cars. Working with large arrays of heterogeneous unstructured data is no longer an extreme problem, because ontologies, cloud technologies, developed methods for working with big data and the use of appropriate digital twinning ensures the rapid development of management decisions [8]. On the basis of digital twins, it is possible to perform semi-physical modeling, which allows the simulation of scenarios of the passage of various processes in the enterprise. The result will be the identification of bottlenecks, failure scenarios, prediction of errors, equipment breakdowns, downtime, etc. In [9] there is a detailed overview of the design of a smart manufacturing system based on digital twinning. Smart manufacture harmoniously combines, controls and regulates the interaction of the following technologies: IIoT, RAD (rapid application development), AI (artificial intelligence), edge computing, cloud, digital twin with MOM (manufacturing operations management) functions, and thus ensures the increased efficiency of advanced enterprises.