In the context of additive manufacturing, the adjustment of process data to individual geometric features offers the potential to further increase manufacturing speed and quality, while being widely underestimated in recent research. Unfortunately, the current non-uniform data handling in the CAD-CAM-Link results in a downstream data loss, that prevents the availability of geometric knowledge from being present at any time to apply the more advanced approaches of adaptive slicing and toolpath generation. Automatic detection of various geometric entities would be beneficial for classifying partial surfaces and volumetric ranges to gain customized informational insights of geometric parameterization. In this work, an enhanced approach of geometric deep learning for the analysis of voxelized engineering parts will be presented to align the inference representations to modeling paradigms for complex design models like architected materials. Although the baseline voxel representation offers distinct advantages in detection accuracy, it comes with an adversely large memory footprint. The geometry discretization leads to high resolutions needed to capture various detail levels that prevent the analysis of fine-grained objects. To achieve efficient usage of 3D deep learning techniques, we propose a 3D-CNN-based feature recognition approach using signed distance field data to limit the needed resolution. This implicit geometric data leverages the advantages of volumetric convolution while alleviating their disadvantages through the use of the continuous signed distance function. When analyzing CAD data for geometric primitive features, a common application task in surface reconstruction of reverse engineering, the proposed methodology achieves a detection accuracy that is in line with the accuracy values achieved by comparable algorithms. This enables the recognition of fine-grained surface instances. The unambiguous shape information extracted could be used in subsequent adaptive slicing algorithms to achieve individual geometry-based hatch generation.
In reengineering technical components, the robust automation of reverse engineering (RE) could overcome the need for human supervision in the surface reconstruction process. Therefore, an enhanced computer-based geometric reasoning to derive tolerable surface deviations for reconstructing optimal surface models would promote a deeper geometric understanding of RE downstream processes. This approach integrates advanced surface information into a deep learning-based recognition framework by explicitly labeling geometric outliers and subsurface boundaries. For this purpose, a synthetic dataset is created that morphs nominal surface models to resemble the macroscopic surface pattern of physical components. For the detection of regular geometry primitives, a 3D-CNN is used to analyze the voxelized components based on signed distance field data. This explicit labeling approach enables surface fitting to derive suitable shape features that fulfill the underlying surface constraints.
Textile reinforcements are increasingly establishing their position in the construction industry due to their high tensile properties and corrosion resistance for concrete applications. In contrast to ribbed monolithic steel bars with a defined form-fit effect, the conventional carbon rovings’ bond force is transmitted primarily by an adhesive bond (material fit) between the textile surface and the surrounding concrete matrix. As a result, relatively large bonding lengths are required to transmit bond forces, resulting in inefficient material utilization. Novel solutions such as tetrahedral profiled rovings promise significant improvements in the bonding behavior of textile reinforcements by creating an additional mechanical interlock with the concrete matrix while maintaining the high tensile properties of carbon fibers. Therefore, simulative investigations of tensile and bond behavior have been conducted to increase the transmittable bond force and bond stiffness of profiled rovings through a defined roving geometry. Geometric and material models were thus hereby developed, and tensile and pullout tests were simulated. The results of the simulations and characterizations could enable the optimization of the geometric parameters of tetrahedral profiled rovings to achieve better bond and tensile properties and provide basic principles for the simulative modeling of profiled textile reinforcements.
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