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Mobilise

Junior research group “simulation methods for the manufacturing and process chain design of hybrid components”

About the project

A central focus within Mobilise is facilitating function-integrated hybrid lightweight design by digital methods and tools, which is being researched in the Open Hybrid LabFactory e. V. (OHLF). A particular challenge for the development of new technologies for the production of functional structures in multi-material design is the development of suitable simulation methods for the design and evaluation of manufacturing processes, process chains and factories. For this purpose, a junior research group "simulation methods for the manufacturing and process chain design of hybrid components" has been established at the OHLF to interlink the competences of the participating research institutions.

Project information

  • Funding: 01/2017-10/2022, Niedersächsisches Ministerium für Wissenschaft und Kultur (MWK)

Project partner

Institute of Forming Technology and Machines

Research objective

Mobilise

The aim of the junior research group is to develop and establish a concept for the integrated computational product and production engineering (icPPE). Within this concept, various individual simulation models are coupled along the product development process. This coupling serves to provide a holistic view of the product development process in order to be able to consider technological, economic and ecological research questions. In addition to the provision of suitable and consistent simulation methods for the design and evaluation of manufacturing processes, process chains, factories and their associated product properties, a further research objective is the transfer of engineering data (simulation data) to the production phase. In conjunction with additional data (sensors, machines) from ongoing production operations and data-driven methods, a resilient production is made possible.

Approach

FEM surrogate model
Quality prediction of a thermoplastic composite by FEM (left) and fast data-driven surrogate modeling (right).

In the context of integrated computational product and production engineering (icPPE), a distinction is made between the three levels of product, process and process chain/factory. At each level, different simulation methods are used with regard to the desired level of detail. Product properties such as mechanical performance (e.g. strength, stiffness) are mainly influenced by structural parameters such as fibre orientation or adhesion. These variables are in turn mainly determined by the manufacturing process. Therefore, the process parameters have a significant influence on the structural parameters and thus on the product properties. The relationship between process and structural parameters is investigated in a virtual process chain using the finite element method (FEM). Since detailed FEM simulations are time-consuming at both product and process level, model reduction methods and data-driven surrogate models are specifically used here. These surrogates enable fast parameter studies to be carried out with sufficient physical detail.
In addition to physical properties, process parameters like cycle times, temperatures and machine pressures can already be estimated using FEM. In combination with machine data, the FEM data obtained at the process level are used as input for the process chain simulation. At the process chain/factory level, the physical detailed consideration of the processes is less relevant, so that mainly agent-based and discrete event simulations are used in conjunction with surrogates from the detailed simulation. The integrated view makes it possible to virtually evaluate effects and interactions at every level during product development in order to accelerate development times and improve prediction quality. In addition, the generation of real-time capable surrogate models based on simulation data from the engineering phase enables model-based quality prediction in operation. This can be used, for example, for inline quality control in order to determine process-dependent quality characteristics without direct measurement (see Figure).

Members

Sebastian Gellrich
s.gellrich(at)tu-braunschweig.de
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