Optimizing Smart Factory Operations: A Methodological Approach to Industrial System Implementation based on OPC-UA

Abstract

The article presents a comprehensive methodology for deploying OPC-UA models as a standard communication protocol, emphasizing their key role in improving near real-time data exchange and operational efficiency within industrial systems. A case study centered on a continuous flow scale system within a grain factory that handles commodities such as corn, soybeans, and wheat, illustrates how OPC-UA significantly improves speed, precision, and consistency in weight measurements, thereby fostering a smarter and more sustainable agricultural future. The primary objective of the study is to provide a roadmap for the development of industrial system controls leveraging OPC-UA architecture. This involves delineating and implementing control modules based on OPC-UA, utilizing cost-effective solutions and high-level programming languages for creating servers and clients (e.g., Python, Java, Android, Node-RED). By seamlessly integrating UML-based design methodologies with OPC-UA, the article advocates for streamlined and standardized development processes, particularly within the scope of Industry 4.0-driven smart factories. The code is available at GitHub: https://github.com/hvelesaca/ OPC-UA-methodology, facilitating further research.