The design and development of composite structures requires precise and robust manufacturing processes. Composite materials such as fiber reinforced thermoplastics (FRTP) provide a good balance between manufacturing time, mechanical performance and weight. In this contribution, we investigate the process combination of thermoforming FRTP sheets (organo sheets) and injection overmolding of short FRTP for automotive structures. The limiting factor in those structures is the bond strength between the organo sheet and the overmolded thermoplastic. Within this process chain, even small deviations of the process settings (e.g., temperature) can lead to significant defects in the structure. A cyber physical production system based framework for a digital twin combining simulation and machine learning is presented. Based on parametric Finite-Element-Method (FEM) studies, training data for machine learning methods are generated and a FEM surrogate is developed. A comparison of different data-driven methods yields information on the estimation accuracy of task-specific data-driven methods. Finally, in accordance with experimental cross tension tests, the investigated FEM surrogate model is able to predict the interface bond strength quality in dependence of the process settings. The visualization into different quality domains qualifies the presented approach as decision support.

Selective laser sintering (SLS) with polymers is currently at the transition stage for the production of functional components and holds great potential to revolutionize conventional production processes. Nevertheless, its application capability is confronted by newly imposed requirements regarding reliability and reproducibility. To safeguard these requirements, a deeper process understanding of material aging mechanisms in polymeric materials is needed. In order to enable the traceability of the materials as well as the identification of defective components with subsequent tracing of the cause, the use of a material marking process represents an alternative. SLS in combination with material marking is proving to be an efficient option for reproducible, high-quality manufacturing based on an increased understanding of the process. In this study, the idea of a marker-based traceability methodology for the purpose of process optimization is presented. Fundamental to the subsequent experimental investigation of the marking agent suitability, this work first focuses on the systematic selection of a suitable marking agent for use in SLS. Based on an analysis of the sinter material to be marked and a set of marking technologies, as well as using the selection methodology, the modified polymer marking technology was evaluated as the most suitable marking technology.

Within product development processes, computational models are used with increasing frequency. However, the use of those methods is often restricted to the area of focus, where product design, manufacturing process, and process chain simulations are regarded independently. In the use case of multi-material lightweight structures, the desired products have to meet several requirements regarding structural performance, weight, costs, and environment. Hence, manufacturing-related effects on the product as well as on costs and environment have to be considered in very early phases of the product development process in order to provide a computational concept that supports concurrent engineering. In this contribution, we present an integrated computational concept that includes product engineering and production engineering. In a multi-scale framework, it combines detailed finite element analyses of products and their related production process with process chain and factory simulations. Including surrogate models based on machine learning, a fast evaluation of production impacts and requirements can be realized. The proposed integrated computational product and production engineering concept is demonstrated in a use case study on the manufacturing of a multi-material structure. Within this study, a sheet metal forming process in combination with an injection molding process of short fiber reinforced plastics is investigated. Different sets of process parameters are evaluated virtually in terms of resulting structural properties, cycle times, and environmental impacts.

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