The object segmentation from the microstructure of a FSW dissimilar weld / Anna Wójcicka, Łukasz Walusiak, Krzysztof Mroczka, Joanna Krystyna Jaworek-Korjakowska, Krzysztof Oprzędkiewicz, Zygmunt Wrobel ... więcej

The object segmentation from the microstructure of a FSW dissimilar weld / Anna Wójcicka, Łukasz Walusiak, Krzysztof Mroczka, Joanna Krystyna Jaworek-Korjakowska, Krzysztof Oprzędkiewicz, Zygmunt Wrobel // ACI Materials Journal [Dokument elektroniczny]. – 2022, Vol. 15, Iss. 3, Spec. Iss., 13 s. : rys., tab., wykr. – Tryb dostępu: https://www.mdpi.com/1996-1944/15/3/1129. – Oznaczenie ref./art.: 1129. – Bibliogr. 29 poz., Abstr. – Special Issue: Friction and Wear of Materials Surfaces. – doi: 10.3390/ma15031129. – ISSN 1944-737X

Friction stir welding (FSW) is an environmentally friendly, solid-state welding technique. In this research work, we analyze the microstructure of a new type of FSW weld applying a two-stage framework ... więcej

Friction stir welding (FSW) is an environmentally friendly, solid-state welding technique. In this research work, we analyze the microstructure of a new type of FSW weld applying a two-stage framework based on image processing algorithms containing a segmentation step and microstructure analysis of objects occurring in different layers. A dual-speed tool as used to prepare the tested weld. In this paper, we present the segmentation method for recognizing areas containing particles forming bands in the microstructure of a dissimilar weld of aluminum alloys made by FSW technology. A digital analysis was performed on the images obtained using an Olympus GX51 light microscope. The image analysis process consisted of basic segmentation methods in conjunction with domain knowledge and object detection located in different layers of a weld using morphological operations and point transformations. These methods proved to be effective in the analysis of the microstructure images corrupted by noise. The segmentation parts as well as single objects were separated enough to analyze the distribution on different layers of the specimen and the variability of shape and size of the underlying microstructures, which was not possible without computer vision support. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.