Description
This topic field focuses on the development and application of data-driven methods for optimizing materials, processes, and components in the field of fiber-reinforced composites. The focus is on the collection, analysis, and intelligent use of process and material data to enhance quality, efficiency, and sustainability throughout the entire value chain. A particular emphasis is placed on the use of artificial intelligence methods for process monitoring and control, for example through in-situ monitoring, automatic defect detection, or data-driven predictions of component properties. By integrating sensor data, simulations, and digital models, innovative approaches such as digital twins, intelligent manufacturing systems, and AI-supported decision-making for composite production are being developed and researched.

Key Areas and Objectives

• Artificial Intelligence and Machine Learning for Composite Application
• In-situ Process Monitoring and Intelligent Sensor Integration
• Digital Twins of Processes and Components
• Data-Driven Process and Quality Optimization
• Automated Defect and Anomaly Detection
• Digitization of Testing and Characterization Methods
• Trustworthy and Explainable AI for Safety-Critical Applications
• Integration of Simulation, Experimentation, and Data Analysis
• Increased Efficiency Through Intelligent Production Systems

Topic Lead
Jan Seiffert, M.Sc.;