Machine Learning Application and Cloud Computing-based Monitoring for Production Management in the IPC 200 Industrial Process Simulator

Abstract

Abstract Industry 4.0 has revolutionized the way industrial processes are managed, introducing concepts such as advanced automation, the Internet of Things (IoT), and machine learning into production and process management. This article presents the successful incorporation of two Industry 4.0 enablers into the IPC 200 Industrial Process Simulator, a key tool in the training of students in the Electronics and Automation program at the Escuela Superior Politécnica de Chimborazo (ESPOCH). To achieve this integration, the SCRUM methodology, known for its effectiveness in managing technological projects, was employed to oversee and facilitate the implementation of the enablers, which were machine learning and cloud computing. Machine learning implementation was carried out using the Q-Learning algorithm for process optimization and data-driven decision-making, while for cloud computing, the IoT platform Ubidots was used. This allowed for greater efficiency and flexibility in managing the simulated processes in the IPC 200. The integration of these enablers opened the doors of innovation to the individuals using the simulator, allowing them to acquire new competencies in industrial process management, and preparing them to face the challenges of Industry 4.0. Furthermore, this improvement in the simulator provided a more advanced and realistic learning platform, resulting in more robust and applicable training for modern industrial environments.

Keywords

• industry 4.0, industrial process simulator, node-red, ubidots, q-learning, scrum.