Optimization of adaptive process parameters for P-PAW lap welding gap of sheet metal based on BP neural network
For the lap welding of 1.2 mm thick SUS304L stainless steel plates and sheets in large LNG fuel film tanks,there are problems that the lap gap will lead to poor weld formation,which seriously affect the safety of the cabin.In this paper,the laser sensor is used to detect the change of the lap gap,welding process tests are designed for different peak currents and welding speeds under different gaps and the influence mechanism of gap on weld forming quality is studied.Based on BP neural network,the topological relationship model between the process parameters and the gap and the weld forming size under different gaps is established.The training samples of the model are obtained through process experiments of pulsed plasma arc welding;the gap adaptive process parameter optimization system based on BP neural network is realized.The test results show that the system realizes the function of real-time process parameter optimization under different lapping gaps,effectively suppresses the void and other defects caused by the change of lapping gap between 0 and 0.6 mm,achieving good welding forming consistency control,and improving the adaptive welding ability of stainless steel plates and sheets in large LNG fuel film tanks.
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