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Abstract
The metal spatter and light intensity of CO2 welding in the vicinity of the melt pool during the short transition of the melt droplet seriously affect the realtime and reliability of weld feature extraction. The mapping relationship between welding pool characteristic parameters and melting depth is established by using BP neural network optimized by genetic algorithm. The results show that the training results and test results of the optimized BP neural network model of genetic algorithm have little error and meet the requirements of precision. The model can well reflect the relationship between the melting depth and the characteristic parameters of the melting pool.
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Details
1 Guilin University Of Aerospace Technology, Guilin, 541004, China
2 Guilin University Of Electronic Technology, Guilin, 541004, China