Issue 53

Y. Lu et alii, Frattura ed Integrità Strutturale, 53 (2020) 325-336; DOI: 10.3221/IGF-ESIS.53.25 331 Factor Wire feed speed Welding speed Arc correction Aluminum plate thickness Correlation degree 0.727116 0.723069 0.679584 0.721059 Table 3: The correlation degree of each CMT welding parameter to the welding deformation amount Prediction of welding deformation based on GA-BPNN From the gray relational analysis, the wire feed speed, welding speed and aluminum plate thickness are three important parameters that affect the welding deformation. Therefore, BP neural network is used to study the complex nonlinear relationship between three welding parameters (wire feed speed, welding speed, aluminum plate thickness) and one output parameter (welding deformation) to predict the welding deformation of aluminum-steel sheet. The neural network includes 3 input node, 5 implicit layer node, and 1 output layer node. he prediction model of welding deformation of aluminum-steel sheet based GA-BPNN is shown in Fig. 5. Figure 5: Prediction model of welding deformation based on GA-BPNN In order to obtain the training samples required for the neural network, 120 sets of CMT seam welding tests were performed on the DP590 steel plate and the AA6061-T6 aluminum alloy plate. Under the condition that the shielding gas flow rate and the preheating temperature are constant, the welding deformation amount under different heat input is obtained by changing the values of the wire feed speed, the welding speed and the aluminum plate thickness process parameters. The measured deformation point is point B in Fig. 3. After establishing the neural network model, based on the 120 sets of data obtained from the experiment, the first 100 groups were selected for training neural network, and the last 20 groups were used for verification. The operation was performed in Matlab 2016a. The predicted outputs of BPNN and GA-BPNN are shown in Fig. 6 and Fig. 7 respectively. Fig. 8 is the comparison of BPNN and GA-BPNN error. Figure 6: BPNN prediction output As can be seen from the comparison of Fig. 6 and Fig. 7, the GA-BPNN predicts the amount of welding deformation closer to the actual deformation amount than the BPNN. It can be seen from Fig. 8 that both the BPNN and the GA- BPNN have fluctuations around the zero point and the GA-BPNN has a smaller fluctuation range. The BPNN prediction