Additive manufacturing of metal components with laser-powder bed fusion is a very complex process, since powder has to be melted and cooled in each layer to produce a part. Many parameters influence the printing process; however, defects resulting from suboptimal parameter settings are usually detected after the process. To detect these defects during the printing, different process monitoring techniques such as melt pool monitoring or off-axis infrared monitoring have been proposed. In this work, we used a combination of thermographic off-axis imaging as data source and deep learning-based neural network architectures, to detect printing defects. For the network training, a k-fold cross validation and a hold-out cross validation were used. With these techniques, defects such as delamination and splatter can be recognized with an accuracy of 96.80%. In addition, the model was evaluated with computing class activation heatmaps. The architecture is very small and has low computing costs, which means that it is suitable to operate in real time even on less powerful hardware.
The thorough description of the influence of the process conduct in powder-bed-based additive manufacturing on the mechanical properties of the fabricated components represents an ongoing challenge. A recent investigation highlighted that a minor safety feature, such as limiting the range of possible laser beam movements, to avoid interactions between the irradiation and emerging smoke and weld splashes, can cause a noteworthy alteration of the mechanical properties. In this study, the tensile characteristics of selective laser-melted stainless steel (1.4404, 316L) fabricated with two different process conducts were investigated, both of which yielded similar relative densities and surface hardness values. It was found that, besides these two characteristics, the tensile strength (yield and ultimate tensile strength) remained stable, whereas the linear elastic properties, as well as the breaking elongation, exhibited great fluctuations. The Young's modulus in the build-plane ranged from 151 to 208 GPa, and the breaking elongation ranged, respectively, from 33% to 43%. Furthermore, it has been found that this anisotropy is an adjustable characteristic and can be modified via two parameters, the rotation angle increment of the irradiation pathways between successive layers and their total admissible range, also referred to as the limitation window.
Laser-Powder Bed Fusion brings new possibilities for the design of parts, e.g., cutter shafts with integrated cooling channels close to the contour. However, there are new challenges to dimensional accuracy in the production of thin-walled components, e.g., heat exchangers. High degrees of dimensional accuracy are necessary for the production of functional components. The aim is to already achieve these during the process, to reduce post-processing costs and time. In this work, thin-walled ring specimens of H13 tool steel are produced and used for the analysis of dimensional accuracy and residual stresses. Two different scanning strategies were evaluated. One is a stripe scan strategy, which was automatically generated and provided by the machine manufacturer, and a (manually designed) sectional scan strategy. The ring segment strategy is designed by manually segmenting the geometry, which results in a longer preparation time. The samples were printed in different diameters and analyzed with respect to the degree of accuracy and residual stresses. The dimensional accuracy of ring specimens could be improved by up to 81% with the introduced sectional strategy compared to the standard approach.
Experimental analyses are performed to determine thermal conductivity, thermal diffusivity and volumetric specific heat with transient plane source method on hollow sphere structures. Single-sided testing is used on different samples and different surfaces. Results dependency on the surface is observed.
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