J Shanghai Jiaotong Univ Sci

J Shanghai Jiaotong Univ Sci ›› 2021, Vol. 26 ›› Issue (2): 239-244.doi: 10.1007/s12204-020-2234-8

• Welding Automation & Computer Technology • Previous Articles     Next Articles

Weld Geometry Monitoring for Metal Inert Gas Welding Process with Galvanized Steel Plates Using Bayesian Network

Weld Geometry Monitoring for Metal Inert Gas Welding Process with Galvanized Steel Plates Using Bayesian Network

MA Guohong (马国红), LI Jian (李健), HE Yinshui (何银水), XIAO Wenbo (肖文波)   

  1. (1. Key Laboratory of Lightweight and High Strength Structural Materials of Jiangxi Province, School of Mechanical and
    Electrical Engineering, Nanchang University, Nanchang 330031, China; 2. School of Environment and Chemical
    Engineering, Nanchang University, Nanchang 330031, China; 3. Key Laboratory of Nondestructive Testing Ministry of
    Education, Nanchang Hangkong University, Nanchang 330063, China)
  2. (1. Key Laboratory of Lightweight and High Strength Structural Materials of Jiangxi Province, School of Mechanical and
    Electrical Engineering, Nanchang University, Nanchang 330031, China; 2. School of Environment and Chemical
    Engineering, Nanchang University, Nanchang 330031, China; 3. Key Laboratory of Nondestructive Testing Ministry of
    Education, Nanchang Hangkong University, Nanchang 330063, China)
  • Online:2021-04-28 Published:2021-03-24
  • Contact: HE Yinshui (何银水) E-mail:heyingshui117@163.com

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Abstract: We present a novel method to monitor the weld geometry for metal inert gas (MIG) welding process with galvanized steel plates using Bayesian network (BN), and propose an effective method of extracting the weld reinforcement and width online. The laser vision sensor is mounted after the welding torch and used to profile the weld. With the extracted weld geometry and the adopted process parameters, a back propagation neural network (BPNN) is constructed offline and used to predict the weld reinforcement and width corresponding to the current parameter settings. A BN from welding experience and tests is presented to implement the decision making of welding current/voltage when the error between the predictive geometry and the actual one occurs. This study can deal with the negative welding tendency to adapt to welding randomness and indicates a valuable application prospect in the welding field.


Key words: galvanized steel plate| weld geometry| laser vision sensor| Bayesian network (BN)| back propagation
neural network (BPNN)

摘要： We present a novel method to monitor the weld geometry for metal inert gas (MIG) welding process with galvanized steel plates using Bayesian network (BN), and propose an effective method of extracting the weld reinforcement and width online. The laser vision sensor is mounted after the welding torch and used to profile the weld. With the extracted weld geometry and the adopted process parameters, a back propagation neural network (BPNN) is constructed offline and used to predict the weld reinforcement and width corresponding to the current parameter settings. A BN from welding experience and tests is presented to implement the decision making of welding current/voltage when the error between the predictive geometry and the actual one occurs. This study can deal with the negative welding tendency to adapt to welding randomness and indicates a valuable application prospect in the welding field.


关键词: galvanized steel plate| weld geometry| laser vision sensor| Bayesian network (BN)| back propagation
neural network (BPNN)

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