Neuartige funktionsbedingte Bauteilgeometrien in geringen Stückzahlen lassen sich mit verschiedenen Methoden der additiv, generativen Fertigung, populärwissenschaftlich 3D- Druck genannt, effizient herstellen. Für den Konstrukteur solcher Bauteile bedeutet dies ebenfalls neuartige Methoden als bisher anzuwenden. Bauteilgeometrien, die hinsichtlich einer Zielgröße optimiert sind, können mit der Topologieoptimierung auf Basis eines FE- Modells rechnerisch ermittelt werden. Während die Topologieoptimierung schon seit längerem bekannt und etabliert ist, war die durchgängige Nutzung einer gemeinsamen Datenbasis häufig durch Hindernisse geprägt. Im vorliegenden Artikel werden die heutigen Möglichkeiten anhand des FE-Systems ANSYS aufgezeigt und hinsichtlich des effizienten praktischen Einsatzes bewertet. / Innovative function-related component geometries in small quantities can be produced efficiently with different methods of additive, generative manufacturing, in a popular science known as 3D printing. For the designer of such components it also means to use other methods as usual. Component geometries optimized regarding to a target size can be calculated using topology optimization based on a FE model. While topology optimization has been known and established for a long time, the consistent use of a common database was often characterized by obstacles. In this article today's possibilities are shown with the FE system ANSYS and evaluated with regard of the efficient practical use.

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<div><b>1. A Closed-Loop Machine Learning and Compensation Framework for Geometric Accuracy Control of 3D Printed Products</b></div><div><b><br></b></div>Additive manufacturing (AM) systems enable direct printing of three-dimensional (3D) physical products from computer-aided design (CAD) models. Despite the many advantages that AM systems have over traditional manufacturing, one of their significant limitations that impedes their wide adoption is geometric inaccuracies, or shape deviations between the printed product and the nominal CAD model. Machine learning for shape deviations can enable geometric accuracy control of 3D printed products via the generation of compensation plans, which are modifications of CAD models informed by the machine learning algorithm that reduce deviations in expectation. However, existing machine learning and compensation frameworks cannot accommodate deviations of fully 3D shapes with different geometries. The feasibility of existing frameworks for geometric accuracy control is further limited by resource constraints in AM systems that prevent the printing of multiple copies of new shapes.<div><br></div><div>We present a closed-loop machine learning and compensation framework that can improve geometric accuracy control of 3D shapes in AM systems. Our framework is based on a Bayesian extreme learning machine (BELM) architecture that leverages data and deviation models from previously printed products to transfer deviation models, and more accurately capture deviation patterns, for new 3D products. The closed-loop nature of compensation under our framework, in which past compensated products that do not adequately meet dimensional specifications are fed into the BELMs to re-learn the deviation model, enables the identification of effective compensation plans and satisfies resource constraints by printing only one new shape at a time. The power and cost-effectiveness of our framework are demonstrated with two validation experiments that involve different geometries for a Markforged Metal X AM machine printing 17-4 PH stainless steel products. As demonstrated in our case studies, our framework can reduce shape inaccuracies by 30% to 60% (depending on a shape's geometric complexity) in at most two iterations, with three training shapes and one or two test shapes for a specific geometry involved across the iterations. We also perform an additional validation experiment using a third geometry to establish the capabilities of our framework for prospective shape deviation prediction of 3D shapes that have never been printed before. This third experiment indicates that choosing one suitable class of past products for prospective prediction and model transfer, instead of including all past printed products with different geometries, could be sufficient for obtaining deviation models with good predictive performance. Ultimately, our closed-loop machine learning and compensation framework provides an important step towards accurate and cost-efficient deviation modeling and compensation for fully 3D printed products using a minimal number of printed training and test shapes, and thereby can advance AM as a high-quality manufacturing paradigm.<br></div><div><br></div><div><b>2. Multiple Imputation and the Bootstrap for the Analysis of Big Data with Missingness</b></div><div><br></div><div>Inference can be a challenging task for Big Data. Two significant issues are that Big Data frequently exhibit complicated missing data patterns, and that the complex statistical models and machine learning algorithms typically used to analyze Big Data do not have convenient quantification of uncertainties for estimators. These two difficulties have previously been addressed using multiple imputation and the bootstrap, respectively. However, it is not clear how multiple imputation and bootstrap procedures can be effectively combined to perform statistical inferences on Big Data with missing values. We investigate a practical framework for the combination of multiple imputation and bootstrap methods. Our framework is based on two principles: distribution of multiple imputation and bootstrap calculations across parallel computational cores, and the quantification of sources of variability involved in bootstrap procedures that use subsampling techniques via random effects or hierarchical models. This framework effectively extends the scope of existing methods for multiple imputation and the bootstrap to a broad range of Big Data settings. We perform simulation studies for linear and logistic regression across Big Data settings with different rates of missingness to characterize the frequentist properties and computational efficiencies of the combinations of multiple imputation and the bootstrap. We further illustrate how effective combinations of multiple imputation and the bootstrap for Big Data analyses can be identified in practice by means of both the simulation studies and a case study on COVID infection status data. Ultimately, our investigation demonstrates how the flexible combination of multiple imputation and the bootstrap under our framework can enable valid statistical inferences in an effective manner for Big Data with missingness.<br></div>

In this thesis, the incorporation of drug loaded mesoporous magnesium carbonate as an excipient for the additive manufacturing of oral tablets by fused deposition modeling was investigated. Cinnarizine, a BCS class II drug, was loaded into the pores of the mesoporous material via a soaking method, corresponding to a drug loading of 8.68 wt%. DSC measurements on the loaded material suggested that the drug was partially crystallized after incorporation, meanwhile the XRD diffractogram implied that the drug was in a state lacking long range order. The drug loaded material was combined with two pharmaceutical polymers, Aquasolve LG and Klucel ELF, and extruded into filaments with a single screw extruder. Filaments of Klucel ELF and drug loaded Upsalite (30:70 wt% ratio) were successfully implemented for the printing oral tablets, in contrast to the Aquasolve LG based filaments which were difficult to print due to thickness variations and non-uniform material distributions. The drug content obtained by TGA suggested drug loadings of 7.71 wt% and 2.23 wt% in the drug loaded Upsalite and tablets respectively. Dissolution studies using an USP II apparatus showed a slower API-release from the tablets in comparison to the crystalline drug, most probably due to slow diffusion of drug species through the polymeric matrix. For future studies, pharmaceutical polymers with higher aqueous solubility should be investigated in order to thoroughly examine the potential of utilizing the immediate release property of Upsalite.

The theme of this doctoral thesis is the design of concrete shell structures with the focus on finding their optimal shape. The optimal shape of a concrete shell is the shape in which for a given load (usually the dead weight of the structure) no significant bending moments are generated in the shell and the structure is in the so-called membrane state. The inspiration for this thesis is the work of Swiss engineer Heinz Isler, who developed the shapes of shell structures using model tests of appropriately loaded flexible membranes. He developed the shell structure for large spans by inverting the resultant shape, which carried its weight almost entirely via membrane forces. The numerical solution of the above experiments using Midas Civil is presented herein. The basic principles of the method are demonstrated on the example of sagged cable. The numerically found shapes are compared with the analytical solution. A shell is designed based on the numerically found shapes and its stress response to dead load is described, particularly in relation to the membrane action. In the next part, the acquired knowledge and methods were used to design three relatively complicated shell structures. Each structure was statically analysed and its static behaviour was described. Structures with perfectly rigid or flexible supports, which simulate real behaviour of the supports, were studied. In the final phase, the results of static analysis of the selected shell were experimentally verified on a physical model in a scale of 1: 55.56. The model has been built using 3D printing. The thesis describes the use of a modelling similarity, the model design, the production process, and the experiment. The load test confirmed the optimal design of the shell structure and the validity of the numerical method for finding their shapes.

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Durch den Einsatz des 3D-Drucks mit Beton kann die Produktivität auf der Baustelle enorm gesteigert, der gesamte Bauablauf optimiert und zugleich geometrische Freiheit ohne zusätzliche Kosten realisiert werden. Jedoch bevor der Beton-3D-Druck eine breite Anwendung finden kann, müssen einige Fragen in Bezug auf die Dauerhaftigkeit sowie die Gebrauchsfähigkeit gedruckter Betonelemente erforscht werden. Im vorliegenden Aufsatz wird die Problematik der Verformungen und Rissbildung bei gedruckten Betonelementen infolge des plastischen Schwindens beleuchtet. Außerdem werden geeignete Messverfahren für die Quantifizierung von gehindertem sowie ungehindertem plastischem Schwinden von gedrucktem Beton vorgestellt.

Mit der neuartigen Bauweise der additiven Fertigung wurden verschiedene Strukturen aus Carbonbeton hergestellt. Die in diesem Beitrag thematisierte Besonderheit ist die Verwendung von Carbonbewehrung mit mineralischer Tränkung, die es ermöglicht, den Verbund zur Betonmatrix zu verbessern und das volle Potential der digitalen Fertigung im Hinblick auf die geometrische Freiheit auszuschöpfen. Nach der Vorstellung des verwendeten 3D-Druckverfahrens und der Tränkung der Bewehrung wird auf die unterschiedlichen Möglichkeiten der Bewehrungsintegration eingegangen. Im Anschluss werden die Ergebnisse der mechanischen Untersuchungen an den mittels der bevorzugten Herstellungsweise gefertigten Prüfkörpern unter Einbeziehen von CT-Aufnahmen diskutiert. Die entwickelte Technologie hat das Potential, den Bauprozess effizienter und umweltfreundlicher zu gestalten.