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.

In this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches.

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.

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.

The construction industry is facing a dual challenge: an increasing demand for new buildings on the one hand and the urgent need to drastically reduce emissions and waste on the other. One promising field of research to face these challenges comprises additive manufacturing (AM) technologies. Through these advanced methods, digital workflows between design and fabrication can be implemented to optimise the form and structure, unlocking new architectural freedom while ensuring sustainability and efficiency. However, to drive this transformation in construction, the new technologies must be investigated in large-scale applications. One of these fast-emerging AM techniques is Shotcrete 3D Printing (SC3DP). The present research documents the 1:1 scale manufacturing process, from digital to real, of a building section utilising SC3DP. A workflow and production steps, spanning from design over manufacturing to assembly, are introduced. The architectural design, reinforced by computational methods, was iteratively refined to adapt to manufacturing constraints. The paper also emphasises the importance of a digital twin in ensuring seamless data integration and real-time adjustments during construction. By incorporating reinforcement techniques such as short rebar insertion and robotic fibre winding, this study demonstrates the structural capabilities achievable with SC3DP. In summary, the implementation of comprehensive digital workflows utilising computational design, automated data acquisition and data flow, as well as robotic fabrication is presented to demonstrate the potential of AM methods in construction. Furthermore, this paper provides a perspective on potential future research paths and opportunities inherent in leveraging the innovative SC3DP technique.