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    Wyświetlanie 1-2 z 2
    Tytuł:
    Microstructural Analysis of Asymmetric Dilution by Rotating Direct Metal Deposition
    Autorzy:
    Choi, Byungjoo
    Lee, Gwang-Jae
    Yeom, Hyun-Ho
    Lee, Moon-Gu
    Jeon, Yongho
    Powiązania:
    https://bibliotekanauki.pl/articles/356931.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    rotating direct metal deposition
    asymmetric dilution
    cross-sectional analysis
    microstructure
    metallurgical composition
    Opis:
    In this study, cross-section analysis was performed on a novel rotating direct-metal deposition method capable of preliminary surface treatment and damage repair of cylindrical inner walls. The cross-sectional shape, microstructure, and metallurgical composition were analyzed to verify feasibility. No defects such as porosity or cracks were found in the cross section, but asymmetric dilution was observed because of the non-coaxial powder nozzle. Microstructural coarsening was confirmed over a higher dilution area by high-magnification optical microscope images. As the dilution ratio was increased, hard carbides in the dendrite were bulkdiffused into inter-dendrite spaces, and the toughness was lowered by Fe penetration into the deposit. Therefore, the increased laser heat input can be modulated to the typical dilution by decreasing the laser scanning velocity.
    Źródło:
    Archives of Metallurgy and Materials; 2019, 64, 2; 535-538
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Defect Detection Using Deep Learning-Based YOLOv3 in Cross-Sectional Image of Additive Manufacturing
    Autorzy:
    Choi, Byungjoo
    Choi, Yongjun
    Lee, Moon-Gu
    Kim, Jung-Sub
    Lee, Sang-Won
    Jeon, Yongho
    Powiązania:
    https://bibliotekanauki.pl/articles/2048889.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    additive manufacturing
    deposition defect
    data augmentation
    YOLOv3
    object detection
    Opis:
    Deposition defects like porosity, crack and lack of fusion in additive manufacturing process is a major obstacle to commercialization of the process. Thus, metallurgical microscopy analysis has been mainly conducted to optimize process conditions by detecting and investigating the defects. However, these defect detection methods indicate a deviation from the operator’s experience. In this study, artificial intelligence based YOLOv3 of object detection algorithm was applied to avoid the human dependency. The algorithm aims to automatically find and label the defects. To enable the aim, 80 training images and 20 verification images were prepared, and they were amplified into 640 training images and 160 verification images using augmentation algorithm of rotation, movement and scale down, randomly. To evaluate the performance of the algorithm, total loss was derived as the sum of localization loss, confidence loss, and classification loss. In the training process, the total loss was 8.672 for the initial 100 sample images. However, the total loss was reduced to 5.841 after training with additional 800 images. For the verification of the proposed method, new defect images were input and then the mean Average Precision (mAP) in terms of precision and recall was 0.3795. Therefore, the detection performance with high accuracy can be applied to industry for avoiding human errors.
    Źródło:
    Archives of Metallurgy and Materials; 2021, 66, 4; 1037-1041
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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