CLASSIFYING INTERNAL DEFECTS IN FORGINGS BASED ON FLAW-DETECTION PATTERNS OBTAINED BY ULTRASOUND

被引:3
|
作者
Nikitin, V. P.
Korotyshev, I. V.
Artyushov, V. N.
Sinitsyn, E. O.
Kudrin, A. A.
机构
[1] Chelaybinsk Metallurgical Combine (ChMK), Chelyabinsk
[2] Chelyabinsk Affiliate of the Ural Forging Company (Uralskaya Kuznitsa), Chelyabinsk
关键词
Porosity; Lamination; Axial Zone; Cylindrical Specimen; Internal Defect;
D O I
10.1007/s11015-008-9062-7
中图分类号
TF [冶金工业];
学科分类号
0806 ;
摘要
A method has been proposed for classifying internal defects in forgings based on the flaw-detection patterns obtained in ultrasonic testing. The reliability of the method is greater than 75%. Also, a program has been developed to classify internal defects in relation to their length and their location through the thickness and width of forgings. The method is valid for flaw-detection patterns in groups I and III. The method is now being used at the Chelyabinsk affiliate of the Ural Forging Company. The method of classification can also be used to identify internal defects in cylindrical specimens obtained by free forging on presses.
引用
收藏
页码:366 / 370
页数:5
相关论文
共 50 条
  • [31] Acoustic-vibration detection for internal defects of magnetic tile based on VMD and BAS
    Huang Q.
    Xie L.
    Yin G.
    Ran M.
    Liu X.
    Zhendong yu Chongji/Journal of Vibration and Shock, 2020, 39 (17): : 124 - 133
  • [32] X-ray-based machine vision technique for detection of internal defects of sterculia seeds
    Xue, Qilong
    Miao, Peiqi
    Miao, Kunhong
    Yu, Yang
    Li, Zheng
    JOURNAL OF FOOD SCIENCE, 2022, 87 (08) : 3386 - 3395
  • [33] A Method for Detection of Internal Defects of Dielectric Materials Based on Pulsed Electro-Acoustic Technique
    L. Xu
    Z. Hou
    H. Kang
    Experimental Mechanics, 2022, 62 : 417 - 426
  • [34] 3D Image Reconstruction of Monocrystalline Silicon Internal Defects based on Ultrasonic Detection
    Wei, Wu
    ming, Qiu Zong
    hong, Huang Qiu
    Min, Zhao
    bo, Liu Yu
    PROCEEDINGS OF 2016 SIXTH INTERNATIONAL CONFERENCE ON INSTRUMENTATION & MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC 2016), 2016, : 951 - 955
  • [35] Automatic detection and localization of internal defects in additively manufactured aluminum alloy based on deep learning
    Dong, Kang
    Ni, Mao
    Liang, Chen
    Chen, Mingzhang
    Wu, Qiang
    Qin, Xunpeng
    Hu, Zeqi
    Hua, Lin
    MEASUREMENT, 2025, 244
  • [36] Detection of near-surface defects in viscoelastic material based on focused ultrasound thermal effects
    Han, Mengyu
    Zheng, Huifeng
    Gao, Yumeng
    Zhou, Zhuangxin
    Liu, Xiangchen
    ULTRASONICS, 2024, 143
  • [37] A Method for Detection of Internal Defects of Dielectric Materials Based on Pulsed Electro-Acoustic Technique
    Xu, L.
    Hou, Z.
    Kang, H.
    EXPERIMENTAL MECHANICS, 2022, 62 (03) : 417 - 426
  • [38] Identifying and Classifying an Ovarian Cyst using SCBOD (Size and Count-Based Ovarian Detection) Algorithm in Ultrasound Image
    Jeevitha, S.
    Priya, N.
    INTERNATIONAL JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING SYSTEMS, 2022, 13 (09) : 799 - 806
  • [39] Characterization and depth detection of internal delamination defects in CFRP based on line laser scanning infrared thermography
    Zhou, Guangyu
    Zhang, Zhijie
    Yin, Wuliang
    Chen, Haoze
    Wang, Luxiang
    Wang, Dong
    Ma, Huidong
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2024, 23 (05): : 3195 - 3210
  • [40] Internal Defects Detection Method of the Railway Track Based on Generalization Features Cluster Under Ultrasonic Images
    Fupei Wu
    Xiaoyang Xie
    Jiahua Guo
    Qinghua Li
    Chinese Journal of Mechanical Engineering, 2022, 35