Machine Learning Integration with PLC for Industrial Process Control
This project focuses on the integration of machine learning techniques with industrial PLC systems for intelligent process monitoring and quality prediction. The main objective is to demonstrate how data-driven models can be combined with classical automation architectures to support better decision-making in industrial environments.
The system uses Python-based machine learning models trained on industrial process data and connects them to a PLC via OPC UA communication. Process variables are collected from the PLC, processed by the ML model, and then used to analyze product quality and system behavior. The project is based on a real industrial dataset related to process quality prediction and shows how analytics can support automation systems.
This work highlights the interaction between industrial control systems and data science, making it relevant for smart manufacturing and Industry 4.0 applications.
Highlights:
- PLC–ML Integration: Communication between PLC and Python using OPC UA.
- Machine Learning Models: Training and evaluation of ML models for quality prediction.
- Industrial Dataset: Use of real process data for realistic analysis.
- Industry 4.0 Concept: Combination of automation and data analytics for smart factories.
Project repository:
Project repository (GitHub)