Laser Powder Bed Fusion von Magnesiumlegierungen

The additive manufacturing process laser powder bed fusion (LPBF) is increasingly used in industrial series production. Compared to other production technologies, LPBF lacks the range of commercially available materials. Magnesium alloys represent one of these unavailable alloys. Nevertheless, lightweight construction and medical applications would strongly benefit from magnesium alloys being used with LPBF, as magnesium alloys are the lightest structural metals and feature both biocompatibility and biodegradability. However, magnesium alloys are difficult to process with LPBF, particularly because of the high vapor pressure at melting temperature, since the interaction of laser radiation and metal vapor can significantly reduce part quality. Within the scope of this work, two suitable strategies for processing magnesium alloys with LPBF are developed. After selecting a suitable powder material, LPBF specimens made of the alloys AZ91 and WE43are analyzed with regard to microstructure and mechanical properties under static load. For both alloys LPBF specimens exhibit a fine microstructure with mostly equiaxed grains in the size of 1-3 μm. For both tensile and compressive loads, strength and ductility are significantly larger than for typical cast components. At the end of this thesis, various technology demonstrators are built to demonstrate the application-related feasibility of LPBF with magnesium alloys

Vorrichtung und Verfahren zur generativen Bauteilfertigung mit mehreren räumlich getrennten Strahlführungen:

The present invention relates to a device and a method for the additive manufacturing of components, in particular for selective laser melting or laser sintering. The device comprises a processing head (7) having a plurality of spatially separated beam guides, via which one or more laser beams can be directed onto a processing plane (8) along spatially separated beam paths, and one or more optical switching devices (4) by means of which the beam path of each laser beam can be switched between the spatially separated beam paths. The device allows one laser beam source (1) to be used for different beam paths or target positions using such a processing head (7), resulting in better utilization of the beam sources and making it possible to irradiate the processing plane (8) corresponding to a desired component geometry using a smaller number of laser beam sources (1)

Geometry-Based Radiation Prediction of Laser Exposure Area for Laser Powder Bed Fusion Using Deep Learning

Laser powder bed fusion (LPBF) is a promising technique used to manufacture complex geometries in a layer-wised manner. Radiation during the LPBF process is influenced by the part geometry, e.g., the overhang angle and the wall thickness. Locally varying radiation can cause deformation of the product after manufacturing. Thus, the prediction of the geometry-caused radiation before the manufacturing can support the evaluation of the design printability to achieve first-time-right printing. In this paper, we present a framework to predict the geometry-based radiation information using a deep learning (DL) algorithm based on the part geometry from computer-aided design (CAD). The algorithm was trained using data from an LPBF-print job consisting of parts with varying overhang angles. Image data, which include the information of radiation, were captured with an optical tomography (OT) camera system that was installed on a LPBF machine used in a laboratory environment. For the DL algorithm, a U-Net based network with mean absolute error (MAE) loss was applied. The training input was binarized OT data representing the contour of the designed geometry. Complementary, the OT data were used as ground truth for the model training. For the application, the design contours of multiple layers were extracted from the CAD file. The result shows the applicability to predict the OT-like radiation by its contour, which has the possibility to show the anomaly due to the part geometry

Verfahren und Vorrichtung zur Bearbeitung einer Werkstoffschicht mit energetischer Strahlung

The invention relates to a method and a device for machining a material layer using energetic radiation, in particular in order to produce three-dimensional components by melting a particulate material in layers. In the method, one or more energetic beams (7) of one or more beam sources (6) are directed onto a layer to be machined and guided over the layer by means of a dynamic beam guidance system in order to machine regions of the layer. The method is characterized in that at least one of the energetic beams (7) is divided into multiple individual beams (9) by modulating the beam over time, said individual beams being directed onto the layer to be machined in a spatially separated manner. The separation is carried out such that the sum of the power of the individual beams (9) corresponds to the power of the respective energetic beam (7) minus power losses caused by the separation process. By using the proposed method and the corresponding device, the beam sources (6) used for the machining processes can be better used such that the proportion of the value-adding process to the entire process time can be maximized in the case of additive manufacturing processes, and the productivity of the manufacturing system can be increased

Geometry-Based Radiation Prediction of Laser Exposure Area for Laser Powder Bed Fusion Using Deep Learning

Laser powder bed fusion (LPBF) is a promising technique used to manufacture complex geometries in a layer-wised manner. Radiation during the LPBF process is influenced by the part geometry, e.g., the overhang angle and the wall thickness. Locally varying radiation can cause deformation of the product after manufacturing. Thus, the prediction of the geometry-caused radiation before the manufacturing can support the evaluation of the design printability to achieve first-time-right printing. In this paper, we present a framework to predict the geometry-based radiation information using a deep learning (DL) algorithm based on the part geometry from computer-aided design (CAD). The algorithm was trained using data from an LPBF-print job consisting of parts with varying overhang angles. Image data, which include the information of radiation, were captured with an optical tomography (OT) camera system that was installed on a LPBF machine used in a laboratory environment. For the DL algorithm, a U-Net based network with mean absolute error (MAE) loss was applied. The training input was binarized OT data representing the contour of the designed geometry. Complementary, the OT data were used as ground truth for the model training. For the application, the design contours of multiple layers were extracted from the CAD file. The result shows the applicability to predict the OT-like radiation by its contour, which has the possibility to show the anomaly due to the part geometry

Vorrichtung und Verfahren zur generativen Bauteilfertigung

Source: WO15003804A1 [EN] The invention relates to a device and method for laser-based, generative component production. The device comprises a processing head (1), with which a plurality of laser beams, separated from each other, are directed adjacent to each other and/or partially overlapping on the processing plane. The processing head (1) is moved by a movement apparatus (9) over the processing plane, while the laser beams, which are separated from each other, are modulated in intensity, independently of one another, in order to obtain the desired exposure geometry. The laser power and the dimensional size of the generative production can be cost-effectively scaled by the device and the related method according to the invention