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In the forming of thermoplastic composite laminates, the temperature‐dependent bending behaviour plays a significant role, in addition to in‐plane tension, in‐plane shear and ply/ply as well as tool/ply friction. The bending properties are decoupled from the in‐plane properties. Classical beam theories are therefore not valid for laminates, as they significantly overestimate the bending stiffness, especially in the molten state. Various approaches to modelling the bending behaviour have been presented in the literature, which can be used to model the out‐of‐plane properties for simulation. With these approaches, a parameter optimisation based on experimental deflection curves is performed through cantilever beam tests. A comparative analysis is then carried out to evaluate the suitability of a temperature and direction dependent modelling of the bending behaviour, the influence of the in‐plane properties and the computation time. [ABSTRACT FROM AUTHOR]

The bond strength between a thermoformed fibre reinforced thermoplastic sheet and an injected polymer is the limiting factor for the structural integrity of overmoulded thermoplastic composites. In this contribution, a simulation based digital twin of the thermoforming process is presented. From numerical parametric studies a reduced order model based on Proper Orthogonal Decomposition (POD) is developed. The combination with machine learning methods enables the real-time computation of arbitrary physical reliable temperature fields with sufficient accuracy to be used for design purposes and as inline quality gates. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 over molded 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. [ABSTRACT FROM AUTHOR]

Friction drilling is a widely used process to produce bushings in sheet materials, which are processed further by thread forming to create a connection port. Previous studies focused on the process parameters and did not pay detailed attention to the material flow of the bushing. In order to describe the material behaviour during a friction drilling process realistically, a detailed material characterisation was carried out. Temperature, strain rate, and rolling direction dependent tensile tests were performed. The results were used to parametrise the Johnson–Cook hardening and failure model. With the material data, numerical models of the friction drilling were created using the finite element method in 3D as well as 2D, and the finite volume method in 3D. Furthermore, friction drilling tests were carried out and analysed. The experimental results were compared with the numerical findings to evaluate which modelling method could describe the friction drilling process best. Highest imaging quality to reality was shown by the finite volume method in comparison to the experiments regarding the material flow and the geometry of the bushing. [ABSTRACT FROM AUTHOR]