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Additive manufacturing (AM) of alkali-activated materials is a promising method for producing ceramic precursors, construction elements and other parts. A recently introduced AM process is laser-induced slip casting of lithium aluminate/microsilica slurries, which yields parts with excellent mechanical strengths. To clarify the underlying mechanisms, μ-Raman spectroscopy was applied to parts produced by the process, and the dissolution and hydration of lithium aluminate was studied inter alia using conventional and in-situ X-ray diffraction. The results show that significant dissolution of lithium aluminate occurs, particularly at increased temperatures during laser interaction, which leads to an increase of pH and precipitation of an akopovaite-like Li-Al-CO3 layered double hydroxide. The increase of the pH is likely to induce dissolution of the microsilica and possibly formation of a hydrous lithium aluminosilicate gel. These observations explain the strength evolution of the studied parts and can also aid the development and improvement of related AM methods.
Thermal transient problems, essential for modeling applications like welding and additive metal manufacturing, are characterized by a dynamic evolution of temperature. Accurately simulating these phenomena is often computationally expensive, thus limiting their applications, for example for model parameter estimation or online process control. Model order reduction, a solution to preserve the accuracy while reducing the computation time, is explored. This article addresses challenges in developing reduced order models using the proper generalized decomposition (PGD) for transient thermal problems with a specific treatment of the moving heat source within the reduced model. Factors affecting accuracy, convergence, and computational cost, such as discretization methods (finite element and finite difference), a dimensionless formulation, the size of the heat source, and the inclusion of material parameters as additional PGD variables are examined across progressively complex examples. The results demonstrate the influence of these factors on the PGD model’s performance and emphasize the importance of their consideration when implementing such models. For thermal example, it is demonstrated that a PGD model with a finite difference discretization in time, a dimensionless representation, a mapping for a moving heat source, and a spatial domain non-separation yields the best approximation to the full order model.
Additive manufacturing of concrete structures is a novel and emerging technology. Freecontouring in civil engineering, which allows for entirely new designs, is a significant advantage. Inthe future, lower construction costs are expected with increased construction speeds and decreasingrequired materials and workers. However, architects and civil engineers rely on a certain quality ofexecution to fulfil construction standards. Although several techniques and approaches demonstratethe advantages, quality control during printing is highly challenging and rarely applied. Due to thecontinuous mixing process commonly used in 3D concrete printing, it is impossible to exclude varia-tions in the dry mixture or water content, and a test sample cannot be taken as a representative samplefor the whole structure. Although mortar properties vary only locally, a defect in one layer duringprinting could affect the entire integrity of the whole structure . Therefore, real-time process monitor-ing is required to record and document the printing process.At the Bundesanstalt für Materialforschung und -prüfung (BAM) a new test rig for the additive man-ufacturing of concrete is built. The primary purpose is measuring and monitoring the properties of amortar during the printing process.The following study investigates an approach for calculating yield stress and plastic viscosity based onexperimentally recorded pressure data. The calculations assume that fresh mortar behaves as a Bing-ham fluid and that the Buckingham-Reiner-equation is applicable. A test setup consisting of rigid pipeswith integrated pressure sensors at different positions is utilized.Monitoring the printing process with different sensors is crucial for the quality control of an ongoingprocess.