Issue 53

Y. Lu et alii, Frattura ed Integrità Strutturale, 53 (2020) 325-336; DOI: 10.3221/IGF-ESIS.53.25 335 Tab. 5 shows the results of the deformation amount of the control test. It can be seen from the table that in the case where the welding process parameters are the same, the aluminum-steel sheet without anti-deformation warps obviously, and the deformation amount is 0.67 mm. The post-weld deformation of the sheet with the deformation amount predicted by the BP neural network is reduced, and the post-weld deformation is 0.36 mm, and The post-weld deformation of the sheet with the deformation amount predicted by the GA-BP neural network is 0.11 mm which is much smaller than the sheet with the deformation predicted by the BP neural network. Therefore, the GA-BP neural network can effectively predict the CMT welding deformation of the aluminum-steel sheet compared with the BP neural network, and the error is controlled within a reasonable range. Wire feed speed (m/min) Welding speed (m/min) Arc correction (%) Aluminum plate thickness (mm) Deformation (mm) No anti-deformation 3.9 0.70 0 1.5 0.67 BP inverse deformation processing 3.9 0.70 0 1.5 0.36 GA-BP inverse deformation processing 3.9 0.70 0 1.5 0.11 Table 5: Test result of welding deformation C ONCLUSION n this paper, the welding process parameters affecting the CMT seam welding deformation of aluminum-steel are studied by orthogonal test and gray relational grade theory. The influence degree of CMT welding process parameters on welding deformation is analyzed. Then BP neural network and GA-BP neural network were used to predict the welding deformation. The final conclusion is as follows: (1) Based on the orthogonal test and the gray relational analysis, the wire feed speed has the greatest influence on the aluminum alloy CMT welding deformation, the welding speed is the second, and the arc correction has the least influence. (2) The BP neural network improved based on genetic algorithm has higher prediction accuracy. The BP neural network prediction error range is -0.029mm~0.011mm, the maximum relative error is 0.029mm, and the minimum relative error is 0.003mm. However, the GA-BP neural network prediction error range is -0.016mm~0.004mm, the maximum relative error is 0.016mm, and the minimum relative error is 2.69*10 -6 mm. (3) The prediction results of BP neural network and GA-BP neural network are applied to the welding of the sheet in the form of anti-deformation. The results show that the deformation of the welded plate is obviously smaller. Moreover, the deformation of the post-weld plate with the anti-deformation amount predicted by GA-BP neural network is smallest, indicating that the GA-BP neural network is more suitable for the prediction of CMT welding deformation of aluminum-steel. In the future, the deformation of aluminum-steel hybrid sheets in more complex assembly forms and under multiple welds will be further studied, and the neural network prediction model proposed in the paper will be used for prediction. R EFERENCES [1] Solecka, M., Kopia, A., Radziszewska, A. and Rutkowski, B. (2018). Microstructure, microsegregation and nanohardness of CMT clad layers of Ni-base alloy on 16Mo3 steel, Journal of Alloys & Compounds, 751, pp. 86-95. [2] Masters, I., Fan, X., Roy, R. and Williams, D. (2012). Modelling distortion induced in an assembly by the self piercing rivet process, Proceedings of the Institution of Mechanical Engineers. Part B: Engineering Manufacture, 226(2), pp. 300-312. [3] Rai, R., De, A., Bhadeshia, H. K. D. H. and Debroy, T. (2011). Review: friction stir welding tools, Science & Technology of Welding & Joining, 16(4), pp. 325-342. [4] Lei, H. Y., Li, Y. B. and Carlson, B. E. (2017). Cold metal transfer spot welding of 1, mm thick aa6061-t6, Journal of Manufacturing Processes, 28, pp. 209-219. I