Sistema Inteligente de Supervisão e Controle de Capacidade em Processos Industriais: Integração de SCADA, IA e Aprendizado de Máquina

Abstract

This study proposes a machine learning-based system for capacity supervision and control in industrial automation. The solution integrates high-precision sensors, programmable logic controllers (PLCs), and a SCADA (Supervisory Control and Data Acquisition) system, enabling real-time monitoring and adjustment of manufacturing processes. The methodology included the development of a C# software in the Visual Studio 2015 environment, interfacing with a Mitsubishi CPU Q03UDV PLC, and the implementation of the system in a production line for practical evaluation. The results demonstrated the system's ability to maintain process capability indices (CpK) above critical limits (1.33) through automatic deviation correction. Key highlights include efficient integration with industrial networks and dynamic adaptation to production variabilities. On the other hand, limitations were identified, such as dependence on robust infrastructure and challenges in environments with high electromagnetic interference. The discussion highlights the potential for scalability, application in other industrial contexts, and the inclusion of advanced algorithms, such as neural networks, to enhance predictive capabilities. Future work suggests exploring more accessible implementations for small and medium-sized enterprises, integration with IoT for predictive maintenance, and sustainability assessments. This research contributes to the advancement of intelligent automation, promoting consistent quality and operational efficiency in manufacturing.