篇名 |
Graphite Classification of Gray Cast Iron in Metallographic via a Deep Learning Approach
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並列篇名 | Graphite Classification of Gray Cast Iron in Metallographic via a Deep Learning Approach |
作者 | Wesley Huang、Zhi-Yuan Su、Chia-Sui Wang、Mark Yeh、Jyh-Horng Chou |
英文摘要 | 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.
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起訖頁 | 889-895 |
關鍵詞 | Feature recognition、Morphology、Metallographic、Gray casting iron、Deep learning networks |
刊名 | 網際網路技術學刊 |
期數 | 202207 (23:4期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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