Resistance Welding Spot Defect Detection with Convolutional Neural Networks

被引：18
|
作者：
Guo, Zhiye [1 ]
Ye, Shaofeng [1 ]
Wang, Yiju [1 ]
Lin, Chun [1 ]
机构：
[1] Xiamen Univ, Sch Aerosp Engn, Xiamen 361005, Peoples R China
来源：
COMPUTER VISION SYSTEMS, ICVS 2017 | 2017年 / 10528卷
基金：
中国国家自然科学基金;
关键词：
Welding spot; Defect detection; Convolutional neural networks;
D O I：
10.1007/978-3-319-68345-4_15
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A convolutional neural network based method is proposed in this paper to classify the images of resistance welding spot. The features of resistance wielding spots are very complex and diverse, which made it difficult to separate the good ones and the bad ones using hard threshold. Several types of convolutional neural networks with different depths and layer nodes are built to learn the features of welding spot. 10 thousand labeled images are used for training and 3 hundred images are used to test the network. As a result, we get a 99.01% accuracy on test images, which is 97.70% better than human inspection.
引用
收藏
页码：169 / 174
页数：6
相关论文
共 50 条
  • [31] Defect detection infused deposition modelling using lightweight convolutional neural networks
    Kuriachen, Basil
    Jeyaraj, Rathinaraja
    Raphael, Deepak
    Ashok, P.
    Sundari, P. Shanmuga
    Paul, Anand
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2025, 141
  • [32] Batch-normalized Convolutional Neural Networks for Defect Detection of the Steel Strip
    Liu, Junliang
    Zhu, Wei
    Yang, Zekun
    ICRSA 2019: 2019 2ND INTERNATIONAL CONFERENCE ON ROBOT SYSTEMS AND APPLICATIONS, 2019, : 1 - 4
  • [33] Improved Convolutional Neural Network for Laser Welding Defect Prediction
    Huang, Weiwei
    Gao, Xiangdong
    Huang, Yuhui
    Zhang, Yanxi
    INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, 2023, 24 (01) : 33 - 41
  • [34] Improved Convolutional Neural Network for Laser Welding Defect Prediction
    Weiwei Huang
    Xiangdong Gao
    Yuhui Huang
    Yanxi Zhang
    International Journal of Precision Engineering and Manufacturing, 2023, 24 : 33 - 41
  • [35] Solar photovoltaic modules hot spot detection based on deep convolutional neural networks
    Wang D.
    Li C.
    Li M.
    Zhang T.
    Zhu R.
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2022, 43 (01): : 412 - 417
  • [36] Online evaluation of resistance spot welding quality and defect classification
    Yang, Weile
    Gao, Perry
    Gao, Xiangdong
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (09)
  • [37] Focal Spot Deconvolution using Convolutional Neural Networks
    Kuntz, Jan
    Maier, Joscha
    Kachelriess, Marc
    Sawall, Stefan
    MEDICAL IMAGING 2019: PHYSICS OF MEDICAL IMAGING, 2019, 10948
  • [38] Artificial neural networks applied to the quality control in alternating-current resistance spot welding
    Fang, Ping
    Tan, Yiming
    Wu, Lu
    Zhang, Yong
    2000, AAAS Press of Chinese Society of Aeronautics and Astronautics, China (21):
  • [39] Defect Detection of Polyethylene Gas Pipeline Based on Convolutional Neural Networks and Image Processing
    Wang, Jun-qiang
    Zha, Sixi
    Sun, Jia-chen
    Wang, Yang
    Lan, Hui-qing
    JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME, 2024, 146 (06):
  • [40] Surface defect detection of voltage-dependent resistors using convolutional neural networks
    Tiejun Yang
    Shan Peng
    Lin Huang
    Multimedia Tools and Applications, 2020, 79 : 6531 - 6546
12345