A digital twin for lightweight thermoplastic composite part production
Simulation- and data-driven digital twins of individual machines and systems already lead to significant efficiency gains through improved production control and machine maintenance, but their promised potential across stages of value chains is still largely untapped. This work proposes a digital twin system on the series production of thermoplastic composite (TPC) structures, which require hierarchized manufacturing steps across diverse production systems and facilities. The need for integration of non-destructive measurements and simulation-based analyses requires flows of materials, data and goods among several institutions. This work reports on the project MAVO digitalTPC, in which the Fraunhofer Institutes IMWS, ICT, IZFP and SCAI jointly develop a distributed digital twin for thermop lastic composite production in an industry-representative multi-institutional setting. In this use case of a digital twin, the heterogeneous microstructure of the composite as well as the influence of the manufacturing parameters place enormous demands on process control and quality assurance. Concerning production, the manufacturing of unidirectional tapes from raw materials and large-scale hybrid injection moulding processes is in the focus. Cognitive sensor technology is integrated to characterize components and detect defects at multiple points along the process chain, while machine data is fully logged. Proposed AI tools add high-level knowledge to the raw data for the tape production and a chain of inter-mapped multi-physics simulations assesses physical stresses, fibre orientations, and further quantities in 3D. These heterogeneous data sources are integrated into a first digital twin platform demonstrator, in which the resolved raw data remains distributed, while meta-data is structured according to an ontology that semantically defines the unique correspondences of individual parts, simulations and measurements in the overall system. Based on this demonstrator, the final system will enable engineers to identify and individualize resources along the overarching real and virtual process chain and simulate production under the real material and process conditions. We show the framework that can overcome the hurdle of missing data architectures and interfaces for data exchange and the lack of inline testing combined simulation- and data based analysis of TPC. In this m anuscript, we explain the methodology: the process, the measurements, the CAE simulations, and the data management. We then demonstrate the current status of the digital twin at the examples of the first meta-data querying and data transfer, a sample analysis for the CAE chain and the non-destructive-sensor integration for feature tracking that will be the fundament for automated hybrid, AI- and rule-based analysis.