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Type: Article × Subject: Monitoring ×

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    In situ detection of cracks during laser powder bed fusion using acoustic emission monitoring
    (Amsterdam : Elsevier, 2022) Seleznev, Mikhail; Gustmann, Tobias; Friebel, Judith Miriam; Peuker, Urs Alexander; Kühn, Uta; Hufenbach, Julia Kristin; Biermann, Horst; Weidner, Anja
    Despite rapid development of laser powder bed fusion (L-PBF) and its monitoring techniques, there is still a lack of in situ crack detection methods, among which acoustic emission (AE) is one of the most sensitive. To elaborate on this topic, in situ AE monitoring was applied to L-PBF manufacturing of a high-strength Al92Mn6Ce2 (at. %) alloy and combined with subsequent X-ray computed tomography. By using a structure borne high-frequency sensor, even a simple threshold-based monitoring was able to detect AE activity associated with cracking, which occurred not only during L-PBF itself, but also after the build job was completed, i.e. in the cooling phase. AE data analysis revealed that crack-related signals can easily be separated from the background noise (e.g. inert gas circulation pump) through their specific shape of a waveform, as well as their energy, skewness and kurtosis. Thus, AE was verified to be a promising method for L-PBF monitoring, enabling to detect formation of cracks regardless of their spatial and temporal occurrence.
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    Methods for Recognition of Colorado Beetle (Leptinotarsa decemlineata (Say)) with Multispectral and Color Camera-sensors
    (Berlin ; Heidelberg : Springer, 2022) Dammer, Karl-Heinz
    At the beginning of an epidemic, the Colorado beetle occur sparsely on few potato plants in the field. A target-orientated crop protection applies insecticides only on infested plants. For this, a complete monitoring of the whole field is required, which can be done by camera-sensors attached to tractors or unmanned aerial vehicles (UAVs). The gathered images have to be analyzed using appropriate classification methods preferably in real-time to recognize the different stages of the beetle in high precision. In the paper, the methodology of the application of one multispectral and three commercially available color cameras (RGB) and the results from field tests for recognizing the development stages of the beetle along the vegetation period of the potato crop are presented. Compared to multispectral cameras color cameras are low-cost. The use of artificial neural network for classification of the larvae within the RGB-images are discussed. At the bottom side of the potato leaves the eggs are deposited. Sensor based monitoring from above the crop canopy cannot detect the eggs and the hatching first instar. The ATB developed a camera equipped vertical sensor for scanning the bottom of the leaves. This provide a time advantage for the spray decision of the farmer (e.g. planning of the machine employment, purchase of insecticides). In this paper, example images and a possible future use of the presented monitoring methods above and below the crop surface are presented and discussed.