Key points are not available for this paper at this time.
Due to its high interlayer strength and application flexibility, Shotcrete 3D Printing (SC3DP) is a promising method for the additive manufacturing of structural concrete components. The printing process is based on a layer-wise material application, conducted along a pre-designed printing path. However, material batch inhomogeneities and environmental alteration lead to varying concrete properties over the production processes. These material irregularities stochastically affect the layer geometry and thus limit the achievable reproducibility and accuracy. To enhance the process stability and improve the dimensional component quality in case of environmental changes, a reliable mapping between the strand geometry and the process and material parameters is fundamental for systematic cross-section adjustment. In this paper, we present an experimental-based approach for attaining a flexible regression model of the cross-section of Shotcrete 3D Printed concrete strands. The width and height of the layer are chosen for the strand representation, which we considered as the main factors for the printing-path planning. Regarding the modelling parameters, we focus on the volume flow parameters of concrete and air, and on the accelerator dosage. These inertia afflicted parameters can provide a consistent strand geometry, while factors of lower latency such as printing speed or spray distance are conserved for online adaptation. Based on the presented proceeding, an adjustable layer height and width model has been successfully used to predict the strand properties. The production of a medium sized sample wall further proves the applicability to the production process. In addition, we demonstrated that the chosen parameters not only affect the geometry but also the mechanical performance of SC3DP-specimens. This is evaluated based on flexural strength measurements. Given the geometrical and mechanical properties, the study defines applicable limits for the investigated parameters.
Lachmayer et al. (Tue,) studied this question.