Metamodels for Hierarchical Topological Data Representation in Intelligent Manufacturing Systems

The kernel of intelligent manufacturing is data utilization, where data can be trained to build AI models to generate optimized control parameter values for production processes. The data includes dynamic (or state) data from sensors, process (control) data from devices/equipment, and quality data from the Manufacturing Execution System (MES). Since the data sampling frequency is high, which causes a large amount of collected data, and the production devices are diverse, which leads to varying communication protocols, it is a challenge to store and display this data. This paper attempts to develop a metamodel-based topology representation of a production scene to store and display the data. The idea is to design metamodels for Ends (for devices), Edges (for production lines), and the Cloud (the central control platform), respectively. Then the model of each specific manufacturing scene is an instantiation of the metamodel. Our work has the following advantages: (1) It can store data at cloud, edge and end, such that it provides fast data querying and supports local computing such as local optimization, and automatically displays their physical topology; (2) it provides a template to describe the topological representation of all kinds of production scenes, such that the description of a specific production scene is an instantiation of the metamodel configured by the collected data; and (3) it provides a way to automatically generate code to connect devices and a database by configuring the meta-models without training or tuning a modern large model, which is almost impossible for most companies. This work serves as the bottom layer of a generated operating system for intelligent manufacturing that provides data service for the upper control layer.