Search

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]

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]