Transactions of Materials and Heat Treatment

Title：Simulation prediction on undercut for selfpiercing riveting of sheet based on BP artificial neural network

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ClassificationCode：TG386

year,vol(issue):pagenumber：2019,44(8):61-65

Abstract：

For the self-piercing riveting of AlMg3 aluminum alloy sheet, the self-piercing riveting process of two layers of AlMg3 aluminum alloy sheet with thickness of 2 mm was simulated by DEFORM-2D and orthogonal experiment, and the forming quality of self-piercing riveting for sheet was predicted based on BP artifical neural network and simulation results. Then, the die depth, height of die boss, die width and riveting speed were taken as the input layer, the under-cut was taken as the output layer, and the BP artificial neural network with three layers of 4-11-1 was established. The predictive value of BP artifical artificial neural network were obtained by using the simulation results as samples for repeated training. Compared with the finite element simulation results, the maximum error between the two methods was 0.97%. In addition, the self-piercing riveting tests were conducted by the self-piercing riveting equipment and die, and the relative error between prediction value of BP artifical neural network and experimental value was 6.57%. Therefore, the correctness and reliability of BP artifical neural network applied to the prediction on the forming quality of self-piercing riveting for sheet are verified which is an important reference on the actual production of enterprises.

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作者简介：周琦（1981-），男，硕士，讲师 E-mail：632992423@qq.com

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