Issue 53

Y. Lu et alii, Frattura ed Integrità Strutturale, 53 (2020) 325-336; DOI: 10.3221/IGF-ESIS.53.25 334 Figure 12: Xu’s results of welding deformation under simulation and experiment. MSE (mm2) RMSE (mm) MAE (mm) BPNN 0.00014 0.01205 0.00953 GA-BPNN 0.00005 0.00686 0.00539 X u’s result 0.233172 0.482879 0.174549 Table 4: The MSE, RMSE, MAE and MAPE using BPNN and GA-BPNN prediction method. Welding deformation control and verification The welding deformation predicted by BPNN and GA-BPNN was applied to the aluminum-steel sheet by the inverse deformation method to verify the effectiveness of the welding deformation prediction. The aluminum-steel sheets were divided into control experiments, which were no anti-deformation treatment group, BPNN anti-deformation treatment group and GA-BPNN anti-deformation treatment group. In the anti-deformation processing group, the same size deformation was applied to the three points A, B, and C of the steel sheet and the aluminum plate before welding which was opposite to the direction of deformation predicted by the neural network. The results of the deformation control experiment are shown in Fig. 13. As can be seen from Fig. 13, the post-weld sheet after the reverse deformation treatment is significantly controlled compared to the post-weld sheet without reverse deformation treatment, and the deformation amount of Fig. 13(c) is smaller than that of Fig. 13(b). (a) Post-weld plate without reverse deformation treatment (b) Post-weld plate of BPNN reverse deformation treatment (c) Post-weld plate for GA-BPNN reverse deformation treatment Figure 13: Contrast test of deformation treatment