Dynamic estimation of joint penetration by deep learning from weld pool image

Yongchao Cheng, Shujun Chen, Jun Xiao, Yu Ming Zhang

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


This work aims at a novel approach to estimate the root-pass penetration towards its feedback control, in which the real penetration is measured by the backside bead width. The major challenge is that it happens under the workpiece and likely cannot be directly observable. The dynamic evolution of the weld pool surface has been analysed to design an active vision method monitoring the pool surface, yet fundamentally correlated to the unobservable penetration. The designed convolutional neural network model is trained, validated, and tested for recognising the weld penetration with satisfactory accuracy.

Original languageEnglish
Pages (from-to)279-285
Number of pages7
JournalScience and Technology of Welding and Joining
Issue number4
StatePublished - 2021

Bibliographical note

Funding Information:
This work was supported by Beijing Municipal Natural Science Foundation: [Grant Number 3192004]; National Natural Science Foundation of China: [Grant Number 51975014], and General Program of Science and Technology Development Project of Beijing Municipal Education Commission: [Grant Number KM201910005034]. This work was supported by the International Research Cooperation Seed Fund of Beijing University of Technology; National Natural Science Foundation of China [grant numbers 51775007, 51975014]; Beijing Municipal Natural Science Foundation [grant number 3192004].

Publisher Copyright:
© 2021 Institute of Materials, Minerals and Mining. Published by Taylor & Francis on behalf of the Institute.


  • Active vision
  • CNN
  • penetration estimation
  • weld pool surface

ASJC Scopus subject areas

  • Materials Science (all)
  • Condensed Matter Physics


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