Advanced Control Systems in Industry 5.0 Enabling Process Mining.

Area of Science:

• Industrial Engineering
• Computer Science
• Artificial Intelligence

Background:

• Industry 5.0 emphasizes human-centric, sustainable, and resilient industrial systems.
• Integrating Artificial Intelligence (AI) into production processes is crucial for optimizing efficiency and quality.
• Existing Business Process Modeling and Notation (BPMN) approaches need enhancement to fully leverage AI in manufacturing.

Purpose of the Study:

• To propose an innovative approach for modeling production management in Industry 5.0.
• To integrate AI and advanced Process Mining (PM) into BPMN for automated decision-making and process optimization.
• To demonstrate a proof of concept for intelligent decision-making in industrial settings.

Main Methods:

• Utilizing the Business Process Modeling and Notation (BPMN) framework.
• Developing an advanced Process Mining (PM) model incorporating supervised and unsupervised AI algorithms.
• Implementing a Decision Support System (DSS) engine for intelligent decision-making procedures.
• Embedding advanced electronic sensing and actuation systems within the PM model.

Main Results:

• A novel approach to model production management integrating AI within BPMN.
• Demonstration of automated decision-making and optimized machine settings/maintenance through advanced PM.
• Theoretical models applied to food processing and energy production sectors showcasing practical applicability.
• Enhanced engineering management elements for digitalized production processes.

Conclusions:

• The proposed framework enables intelligent decision-making and self-adapting control systems in modern industrial environments.
• This integration of AI, PM, and BPMN facilitates improved product quality supervision and production efficiency.
• The research provides a foundation for advancing Industry 5.0 through smart, automated production management.