Graphite Classification of Gray Cast Iron in Metallographic via a Deep Learning Approach,ERICDATA高等教育知識庫
高等教育出版
熱門: 羅文君  簡淑芸  Yeonjoo Lim  Jong-Hyouk Lee  Beyond 5G  SDGs  
高等教育出版
首頁 臺灣期刊   學校系所   學協會   民間出版   大陸/海外期刊   政府機關   學校系所   學協會   民間出版   DOI註冊服務
篇名
Graphite Classification of Gray Cast Iron in Metallographic via a Deep Learning Approach
並列篇名
Graphite Classification of Gray Cast Iron in Metallographic via a Deep Learning Approach
作者 Wesley HuangZhi-Yuan SuChia-Sui WangMark YehJyh-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.

 

起訖頁 889-895
關鍵詞 Feature recognitionMorphologyMetallographicGray casting ironDeep 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 10.53106/160792642022072304023  複製DOI
QR Code
該期刊
上一篇
Detection and Blocking Method against DLL Injection Attack Using PEB-LDR of ICS EWS in Smart IoT Environments
該期刊
下一篇
Deep Learning-based Attacks on Masked AES Implementation

高等教育知識庫  閱讀計畫  教育研究月刊  新書優惠  

教師服務
合作出版
期刊徵稿
聯絡高教
高教FB
讀者服務
圖書目錄
教育期刊
訂購服務
活動訊息
數位服務
高等教育知識庫
國際資料庫收錄
投審稿系統
DOI註冊
線上購買
高點網路書店 
元照網路書店
博客來網路書店
教育資源
教育網站
國際教育網站
關於高教
高教簡介
出版授權
合作單位
知識達 知識達 知識達 知識達 知識達 知識達
版權所有‧轉載必究 Copyright2011 高等教育文化事業有限公司  All Rights Reserved
服務信箱:edubook@edubook.com.tw 台北市館前路 26 號 6 樓 Tel:+886-2-23885899 Fax:+886-2-23892500