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Accurate Numerical Computation of Hot Deformation Behaviors by Integrating Finite Element Method with Artificial Neural Network

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Abstract

Accurate flow behaviors characterization plays a decisive role in the design and optimization of hot plastic forming processes with finite element method (FEM). In this study, the hot compression tests at elevated temperature were performed on a Gleeble 3500 thermo-physical test machine to acquire the true stress-strain data of GH4169 superalloy. Subsequently, an artificial neural network (ANN) with back-propagation algorithm was employed to learn the experimental true stress-strain data and then predict the constitutive relationships outside experimental conditions. It was revealed that the ANN can precisely track and predict the flow behaviors of GH4169 superalloy. The optimally-constructed and well-trained ANN model was written into a general finite element (FE) software platform via a user defined material subroutine programmed in Fortran language. Finally, the simulated hot compression tests with the FE model implanted ANN model were conducted. The intercomparisons between the experimental and simulated stroke-load curves revealed that integration of FEM with ANN is a feasible approach to conduct quality numerical computation for the varied hot plastic forming processes.

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Authors and Affiliations

  1. Minnan Science and Technology Institute, FuJian Normal University, Quanzhou, Fujian, 362-332, China

    Xiehua Yu, Xiaoyun Zhang, Meilong Chen, Fengfei Kuang & Yuan Wang

  2. School of Information Science and Engineering, Xiamen University, Xiamen, Fujian, 361-005, China

    Xiehua Yu & Linmao Deng

  3. TPV Display Technology (XiaMen) Co., Ltd., Xiamen, Fujian, 361-101, China

    Linmao Deng

Authors
  1. Xiehua Yu
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  2. Linmao Deng
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  3. Xiaoyun Zhang
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  4. Meilong Chen
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  5. Fengfei Kuang
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  6. Yuan Wang
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Correspondence to Xiehua Yu.

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Yu, X., Deng, L., Zhang, X. et al. Accurate Numerical Computation of Hot Deformation Behaviors by Integrating Finite Element Method with Artificial Neural Network. Int. J. Precis. Eng. Manuf. 19, 395–404 (2018). https://doi.org/10.1007/s12541-018-0047-6

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  • DOI: https://doi.org/10.1007/s12541-018-0047-6

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