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In the production of hybrid composites made of thermoplast and metal sheets, certain limitations in the linear forming process can be overcome through a free kinematic forming process. This process involves distributing the thermoplast press mass using a tumbling or cyclic rolling movement of the forming tool. Due to this special kinematics, only a small part of the tool is ever in engagement with the workpiece, so the component is formed incrementally. Therefore, the final geometry does not have to be created in one stroke but can be carried out step by step in several partial strokes. In order to determine the optimal trajectory of the forming tool, the kinematic interplay between the workpiece, forming tool, and toolpath should be numerically captured in a mathematical model. The specific toolpath depends on the component being formed, as well as the actual geometry of the forming tool. In this research, the free kinematic forming process is modeled and the optimal toolpath is determined using a genetic algorithm. The optimal toolpath enables a uniform distribution of the thermoplast to a specified thickness and collisions between the upper and lower tool are avoided. This enables key insights into the free kinematic forming process and opens up further avenues of research for this innovative and future‐oriented manufacturing concept. [ABSTRACT FROM AUTHOR]

In the design and production of press part components, the tool development process is an essential step to fulfil the required quality criteria. Conventionally, this is achieved based on expert knowledge to design the effective surface with respect to the multitude of physical, procedural, and human influences. Thus, several iterations in the tool development process are usually required, which are costly and can lead to bottlenecks within product design cycles. To accelerate this tool design, we propose a diffusion model architecture to inversely design the necessary effective tool surface given a desired geometry of the press part. This diffusion model is able to reduce the generalization issues of classical machine learning approaches by leveraging the attention mechanism both in the spatial and temporal dimension of the underlying forming process. The applicability of a similar diffusion model has already been shown in previous applications for the inverse‐design of metamaterials and this work further demonstrates diffusion models as a suitable model candidate for the inverse‐design of 3D‐geometries. For model training, finite element simulations containing the time series of deformation states during the forming process were used. Furthermore, different geometry variations of part and tool as well as relevant press process parameters were used in the training. With the procedure demonstrated in this study, a future‐oriented support for the tool development process has been shown, enabling further developments towards a time‐ and cost‐efficient production of press tools. [ABSTRACT FROM AUTHOR]

Hybrid structures are increasingly relevant for lightweight design and functional components. Various manufacturing techniques for creating hybrid components exist, often focusing on combining metal and polymer components. The present study examines a novel approach for manufacturing hybrid functional structures by directly printing thermoplastic onto aluminum profiles using closed‐cell aluminum foam, with the mechanical interlocking resulting in a structural bond. The filling of pores in the aluminum foam with the polymer affects the bond strength of this hybrid compound. Within a simulation‐based process design the additive manufacturing process parameters are investigated using a computational fluid dynamics (CFD) model with Fluid‐Structure Interaction for the pore‐filling process and a finite element (FE) model to evaluate the resulting bond strength. The material throughput, the nozzle distance, and the polymer temperature are the key process parameters taken into account. A parameterized, virtual foam model is used within this framework, where a linear movement of the extruder nozzle and the resulting polymer strand is investigated. This method includes the mesh conversion from the CFD results to the FE mesh for structural analysis by exporting the iso‐surface for the polymer geometry embedded in the pores of the aluminum foam structure. This approach made it possible to investigate the phenomena that occur in the process in more detail, particularly to quantify the influence of air inclusions. Thus, a decisive contribution to the hybridization of aluminum foams has been made, which opens up further potential for research and application in the long term. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) enables the production of complex and customised components with minimal waste. However, to achieve the desired geometry and quality, a lot of process optimisation is needed (often by experimental trial and error). Especially, in the case of AM with concrete materials, the time‐dependent behaviour of the fresh concrete is a large challenge to achieve process stability. Simulations taking into account the time‐dependent material are able to predict the printing result. Since these simulations are very time consuming, a proper orthogonal decomposition (POD)‐based reduced order model for AM is presented. Based on snapshots along the build‐up process, a surrogate model is constructed that is able to predict the printing result with respect to the underlying process and materials. Inspired by the POD approximation, a novel surrogate modelling approach using the initial waypoints of the printing trajectory as input is investigated. In this case, it is possible to analyse the manufacturing process for arbitrary geometries. The proposed method is demonstrated for the use case of additive manufacturing in construction. [ABSTRACT FROM AUTHOR]

Recently, physics‐informed neural networks (PINNs) have been effectively utilized in a wide range of problems within the domains of applied mathematics and engineering. In PINNs, the governing physical equations are directly incorporated into the loss function of the network and a conventional labeled dataset is not required for its training. In order to successfully simulate the additive manufacturing processes with concrete, a novel process‐based FE‐simulation has been developed where the Drucker–Prager plasticity model is used as the material model. In this work, we will examine the deployment of a PINN to substitute the Newton–Raphson iterations that occur in the return‐mapping algorithm of the Drucker–Prager plasticity model. [ABSTRACT FROM AUTHOR]

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]

Additive manufacturing in large‐scale construction is an ongoing research topic that shows significant potential to overcome the challenges in terms of efficient material usage and process automation in construction. A large challenge in deposition based additive manufacturing processes of concrete material is to ensure the structural stability while printing. Due to the weak material properties of the fresh concrete, it has to be ensured that during the printing process the not fully cured printed structure is able to carry its own weight. This requires process stabilization and a proper process control to prevent a collapse of the structure. Therefore, a numerical model of the printing process that takes into account the time dependent material behavior of the applied concrete as well as the printing path and the process parameters is necessary to support the process planning. Within the framework of project B04 of the collaborative research center TRR277 – Additive Manufacturing in Construction, a novel path‐based finite element simulation was developed in which the simulated geometry is constructed directly from the printing trajectory. Additionally, this approach allows the time‐dependent material properties of fresh concrete to be modeled directly and efficiently into the mesh of the printed structure. Since the computation of large scale printing processes with finite element simulations is quite expensive, there exists a need for a much faster computational model. In this contribution, the implementation of a surrogate model based on a neural network and its deployment to optimize the interlayer waiting time is presented. [ABSTRACT FROM AUTHOR]

To further the accuracy of a virtual process, links between sequenced numerical methods are established to create a process chain, which considers all influences on a manufactured part in its early design stages. In order to transform numerical results into initial conditions of a preceding simulation method, the data is "mapped" from a source to a target finite element mesh. The data transfer depends heavily on the difference in mesh discretization as well as the utilized interpolation method. Errors occur using different inter‐ and extrapolation methods. This contribution aims to establish a hybrid interpolation method to transfer tensorial stress data between FEM meshes without using an external solver. The applied method consists partly of a radial basis and of a weighted areas interpolation. [ABSTRACT FROM AUTHOR]

The manufacturing of hybrid composites has an important impact on the final structural properties. Manufacturing induced defects influence the mechanical performance and thus need to be considered for the structural design. In this contribution, we present a data‐driven approach for the structural analysis of hybrid metal‐plastic composites. Based on numerical analyses, a surrogate model based on Proper Orthogonal Decomposition and Machine Learning is derived that can be implemented in early design optimizations. [ABSTRACT FROM AUTHOR]

In this contribution, we present a model reduction approach for hyperelastic materials based on Proper Orthogonal Decomposition. A separation of the solution into time and space dependent functions is performed, which allows to compute reduced solutions within real time. The functionality of the method is demonstrated by a tensile test and is further applied to a stochastic analysis and a multi-parametric analysis involving non-linear material behaviour and large deformations. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

A stochastic homogenization scheme for the computation of effective elastic properties for damaged micro-heterogeneous materials is presented. The statistics is captured by uncertain material properties as well as stochastic interpolation between the upper and lower Hashin-Shtrikman bounds. The resulting multidimensional stochastic problem is solved via sophisticated Monte Carlo methods and applied to Ultra-High-Performance-Concrete. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]