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 reinforceme...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.展开更多
基金the National Natural Science Foundation of China(No.51665037)the Open Fund of the Key Laboratory of Lightweight and High Strength Structural Materials of Jiangxi Province(No.20171BCD40003)the Open Fund of the Key Tahoratory of Nondestructive Testing Ministry of Education,Nanchang Hangkong University of China.(No.EW201980090)。
文摘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.
