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Research

A05 Adaptive models for in-line process monitoring and control

Project Area

A05

Project Leaders

Steffen Ihlenfeldt

Alexander Dementyev

Cooperation Projects

A05 Adaptive models for in-line process monitoring and control
Summary
The DediGrad subproject A05 develops data-driven, real-time capable models for in-line monitoring and control of complex manufacturing processes for graded structures. The focus lies on virtual sensors that capture, predict, and continuously adapt multidimensional physical fields of material properties and geometry to changing process conditions. By applying machine-learning methods, in particular generative AI and explainable models, the project seeks to create adaptive prediction systems with high reliability. The aim is to integrate these models into industrial process chains in order to enable stable, transparent, and reproducible manufacturing of graded components.
001 Objectives

Adaptive Process Monitoring

  • Development of real-time data-driven models for in-line process monitoring
  • Integration of the models into virtual sensor systems with adaptation functions
  • Ensuring explainability and reliability of the predictions
  • Derivation of requirements for the future control of highly complex manufacturing processes
002 Scientific Questions

Real-Time Process Modelling

  • How can data-driven models be developed for the in-line monitoring of multidimensional physical fields of material properties and geometry?
  • How should these models be integrated into virtual sensors for industrial application?
  • How should adaptation mechanisms be designed to compensate for ageing, wear, and variations in material properties?
  • Which prerequisites are required for the future control of highly complex processes for manufacturing graded components?

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Shaping the Future of Adaptive Lightweight Structures.