The competence field Process Simulation focuses on the processing of fiber reinforced polymer composites, in particular, the simulation and optimization of manufacturing processes used to create fiber reinforced polymer composite parts. It plays a key role in the digitalization of composite processing chains by creating data and supplementing traditional data collected via experimental methods. To cope with the complexities of multiscale simulation and the ever- increasing demand on computing resources, machine-learning methods are applied to increase the speed of simulations while preserving or even improving accuracy.
All major manufacturing processes for thermoplastic and thermoset based polymer composite manufacturing processes are considered, including thermoforming of organo sheets, resin injection processes, joining of thermoplastic composites (induction welding), winding and tape laying of unidirectional fiber reinforced composites as well as the processing of extruded/pultruded and compression molded parts. Three main features form the fundamental basis of the research work carried out in this competence field. The first is material characterization, which aims to find out how the material behavesin both fluid and solid form under the various physical conditions to which it is subjected.. The second is material digitization in form of material model development, which aims to define and describe the characterized material behavior mathematically. The third is simulation model development, which involves the definition and development of the simulation methods to be used (e.g. type of finite element method, type of elements and which physics should be included in the simulation model). Based on the process simulation model, simulative parameter studies can be carried out where the process, material (prepreg or semi-finished product) and geometric (tool and part shape) parameters can be optimized. On the one hand, parameters for effective production are determined, while on the other hand, local material properties present in the component are predicted and digitally documented and can be made available for further studies and analyses along the computer aided engineering (CAE) simulation chain.
In summary, important research objectives are the new and further development of material characterization and machine-learning-assisted finite-element-based simulation methods, including material modelling, for fiber reinforced polymer composite manufacturing processes.
Herstellprozesssimulation zur Vorhersage der Faltenbildung in der Prepreg-Autoklav-Fertigung
Spritzgussbauteile aus kurzfaserverstärkten Kunstsoffen: Methoden der Charakterisierung und Modellierung zur nichtlinearen Simulation von statischen und crashrelevanten Lastfällen
Zur Simulation der Prozesskette von Harzinjektionsverfahren
Zur methodischen Anwendung der Simulation der Harzinjektionsverfahren
An experimental characterization of wrinkling generated during prepreg autoclave manufacturing using caul plate
Advanced process simulation of compression molded carbon fiber sheet molding compound (C-SMC) parts in automotive series applications
Transverse liquid composite moulding processes for advanced composites material manufacturing
Material Characterization and Compression Molding Simulation of CF-SMC Materials in a Press Rheometry Test
Polarization imaging for surface fiber orientation measurements of carbon
fiber sheet molding compounds
A combined experimental–numerical approach for permeability characterization of engineering textiles
Machine learning for polymer composites process simulation – a review
Influence of polymer matrix on the induction heating behavior of CFRPC laminates
Computational modelling and analysis of transverse liquid composite moulding processes