國家衛生研究院 NHRI:Item 3990099045/14679
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 12189/12972 (94%)
造訪人次 : 967303      線上人數 : 872
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋
    主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於NHRI ‧ 管理 到手機版
    國家衛生研究院 NHRI > 癌症研究所 > 其他 > 期刊論文 >  Item 3990099045/14679


    請使用永久網址來引用或連結此文件: http://ir.nhri.org.tw/handle/3990099045/14679


    題名: Vickers hardness value test via multi-task learning convolutional neural networks and image augmentation
    作者: Cheng, WS;Chen, GY;Shih, XY;Elsisi, M;Tsai, MH;Dai, HJ
    貢獻者: National Institute of Cancer Research
    摘要: Featured Application Feature Applications: This paper proposes a data-driven approach based on convolutional neural networks to measure the Vickers hardness value directly from the image of the specimen to get rid of the requirement of the manually generation of indentations for measurement. Hardness testing is an essential test in the metal manufacturing industry, and Vickers hardness is one of the most widely used hardness measurements today. The computer-assisted Vickers hardness test requires manually generating indentations for measurement, but the process is tedious and the measured results may depend on the operator's experience. In light of this, this paper proposes a data-driven approach based on convolutional neural networks to measure the Vickers hardness value directly from the image of the specimen to get rid of the aforementioned limitations. Multi-task learning is introduced in the proposed network to improve the accuracy of Vickers hardness measurement. The metal material used in this paper is medium-carbon chromium-molybdenum alloy steel (SCM 440), which is commonly utilized in automotive industries because of its corrosion resistance, high temperature, and tensile strength. However, the limited samples of SCM 440 and the tedious manual measurement procedure represent the main challenge to collect sufficient data for training and evaluation of the proposed methods. In this regard, this study introduces a new image mixing method to augment the dataset. The experimental results show that the mean absolute error between the Vickers hardness value output by the proposed network architecture can be 10.2 and the value can be further improved to 7.6 if the multi-task learning method is applied. Furthermore, the robustness of the proposed method is confirmed by evaluating the developed models with an additional 59 unseen images provided by specialists for testing, and the experimental results provide evidence to support the reliability and usability of the proposed methods.
    日期: 2022-10-22
    關聯: Applied Sciences. 2022 Oct 22;12(21):Article number 10820.
    Link to: http://dx.doi.org/10.3390/app122110820
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2076-3417&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000880871400001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85141874503
    顯示於類別:[其他] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    ISI000880871400001.pdf3275KbAdobe PDF118檢視/開啟


    在NHRI中所有的資料項目都受到原著作權保護.

    TAIR相關文章

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