English  |  正體中文  |  简体中文  |  Items with full text/Total items : 18054/20253 (89%)
Visitors : 24249423      Online Users : 544
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/34626


    標題: Graphite Classification of Gray Cast Iron in Metallographic via A Deep Learning Approach
    作者: Huang, Wesley
    Su, Zhi-Yuan
    Wang, Chia-Sui
    Yeh, Mark
    Chou, Jyh-Horng
    貢獻者: Department of Information Management, Chia Nan University of Pharmacy & Science
    Department of Multimedia and Game Development, Chia Nan University of Pharmacy & Science
    National Sun Yat Sen University
    Feng Chia University
    關鍵字: neural-network
    yolo
    日期: 2022
    上傳時間: 2023-12-11 14:01:22 (UTC+8)
    出版者: LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV
    摘要: In addition to measurements of physical and mechanical properties, quality inspections also include metallographic analyses. When gray casting iron material, different manufacturing processes cause different microstructures in the material, whose metallographic images also perform large differences. The metallographic properties of gray iron can be divided into six types (from Type A to Type F). The proportion of types will influence the strength, wear resistance, and lifetime of specimens. The determination of type is usually dependent on manual judgments. In this study, two approaches were developed to analyze six metallographic types of gray casting iron. The first approach was to determine the type according to features of the detected particles in the metallographic materials by morphology algorithm. Types A, C, and F could be identified with the shape factor (SF) of gray casting iron. Then, the remained part could be identified using average grayscale values of the part-region of the metallographic material. Second approach was to identify Types A, C, and F with SF method and then identify the remaining part through the classification of the YOLO V3 deep learning algorithm. The results showed that the second approach performed more suitably in identifying the types of metallographic of gray casting iron.
    關聯: Journal of Internet Technology, v.23, n.4, pp.889-895
    Appears in Collections:[資訊管理系] 期刊論文
    [多媒體與遊戲發展系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    2740-3730-1-PB.pdf667KbAdobe PDF42View/Open
    index.html0KbHTML89View/Open


    All items in CNU IR are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback