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.

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.

No abstract

Overmoulding of thermoplastic composites combines the steps of thermoforming and injection moulding in an integrated manufacturing process. The combination of continuous fibre-reinforced thermoplastics with overmoulded polymer enables the manufacturing of highly functionally integrated structures with excellent mechanical properties. When performed as a one-shot process, an economically efficient manufacturing of geometrical complex lightweight parts within short cycle times is possible. However, a major challenge in the part and process design of overmoulded thermoplastic composites (OTC) is the assurance of sufficient bond strength between the composite and the overmoulded polymers. Within the framework of a simulation-based approach, this study aims to develop a methodology for predicting the bond strength in OTC using simulation data and a numerical model formulation of the bonding mechanisms. Therefore, a modelling approach for the determination of the bond strength depending on different process parameters is presented. In order to validate the bond strength model, specimens are manufactured with different process settings and mechanical tests are carried out. Overall, the results of the numerical computation are in good agreement with the experimentally determined bond strength. The proposed modelling approach enables the prediction of the local bond strength in OTC, considering the interface conditions and the processing history.