Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

被引:26
作者
Trzepiecinski, Tomasz [1 ]
Lemu, Hirpa G. [2 ]
机构
[1] Rzeszow Univ Technol, Dept Mat Forming & Proc, Al Powst Warszawy 8, PL-35959 Rzeszow, Poland
[2] Univ Stavanger, Fac Sci & Technol, N-4036 Stavanger, Norway
关键词
elastic strain; genetic algorithm; material properties; perceptron-based prediction; springback; steel sheet metal; BENDING PROCESS; HYPERBOLIC TANGENT; ANALYTICAL-MODEL; FINITE-ELEMENT; NEURAL-NETWORK; BEHAVIOR; DESIGN;
D O I
10.3390/ma13143129
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
This paper presents the results of predictions of springback of cold-rolled anisotropic steel sheets using an approach based on a multilayer perceptron-based artificial neural network (ANN) coupled with a genetic algorithm (GA). A GA was used to optimise the number of input parameters of the multilayer perceptron that was trained using different algorithms. In the investigations, the mechanical parameters of sheet material determined in uniaxial tensile tests were used as input parameters to train the ANN. The springback coefficient, determined experimentally in the V-die air bending test, was used as an output variable. It was found that specimens cut along the rolling direction exhibit higher values of springback coefficient than specimens cut transverse to the rolling direction. An increase in the bending angle leads to an increase in the springback coefficient. A GA-based analysis has shown that Young's modulus and ultimate tensile stress are variables having no significant effect on the coefficient of springback. Multilayer perceptrons trained by back propagation, conjugate gradients and Lavenberg-Marquardt algorithms definitely favour punch bend depth under load as the most important variables affecting the springback coefficient.
引用
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页数:16
相关论文
共 39 条
  • [1] Springback prediction and elasticity modulus variation
    Aerens, Richard
    Vorkov, Vitalii
    Duflou, Joost R.
    [J]. 18TH INTERNATIONAL CONFERENCE ON SHEET METAL, SHEMET 2019 - NEW TRENDS AND DEVELOPMENTS IN SHEET METAL PROCESSING, 2019, 29 : 185 - 192
  • [2] Albrut A, 2006, ARCH CIV MECH ENG, V6, P12, DOI [10.1016/S1644-9665(12)60237-4, DOI 10.1016/S1644-9665(12)60237-4]
  • [3] [Anonymous], **NON-TRADITIONAL**
  • [4] [Anonymous], **NON-TRADITIONAL**
  • [5] [Anonymous], 2010, J Achievem Mater Manuf Eng
  • [6] [Anonymous], **NON-TRADITIONAL**
  • [7] Bishop C.M., 1995, Neural networks for pattern recognition
  • [8] Investigation on the influence of damage to springback of U-shape HSLA steel plates
    Dai, Hong-Liang
    Jiang, Hao-Jie
    Dai, Ting
    Xu, Wei-Li
    Luo, Ai-Hui
    [J]. JOURNAL OF ALLOYS AND COMPOUNDS, 2017, 708 : 575 - 586
  • [9] DEFILIPPIS LAC, 2016, MATERIALS, V0009
  • [10] Dib M., 2018, ENG APPL NEURAL NETW, V893, P169