Graphite Classification of Gray Cast Iron in Metallographic via A Deep Learning Approach

doi:10.53106/160792642022072304023

Chia Nan University of Pharmacy & Science Institutional Repository > 行政單位 > 123 > Item 310902800/34626

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

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