Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (4): 140-143.doi: 10.11896/jsjkx.210300238

• Database & Big Data & Data Science • Previous Articles     Next Articles

Prediction Method of Structural Static Performance Based on Data Learning

ZHAO Hang1, TONG Shui-guang1, ZHU Zheng-zhou2   

  1. 1 School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China;
    2 School of Software & Microelectronics, Peking University, Beijing 102600, China;
  • Received:2021-03-23 Revised:2021-09-09 Published:2022-04-01
  • About author:ZHAO Hang,born in 1994,postgra-duate.His main research interests include optimization of mechanical structure and data learning.ZHU Zheng-zhou, born in 1979,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include personalized recommendation in the big data environment and educational big data.
  • Supported by:
    This work was supported by the Zhejiang Province Key R&D Program(2019C01057).

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Abstract: Aiming at the high cost of establishing a prediction model in the current mechanical structure optimization, a prediction method of structural static performance based on data learning is proposed.The cantilever beam is taken as the research object, and the finite element model is established to obtain the displacement field data of the simulation results.Then the boundary condition-displacement field surrogate model is constructed.The results show that the trend of displacement field distribution is consistent with the actual situation, and the relative error of the maximum displacement is -0.02% and -0.47% under the load of 1000N and 1600N, respectively.The influences of the magnitude of the uniform force and the position of the concentrated force on the displacement field prediction are discussed.The results show that the prediction error increases with the increase of load amplitude.Compared with the uniform force, the prediction error under the concentrated force load is larger, and the error is larger when the loading position is near the edge.In the inversion problem, the displacement fields are taken as the input, the uniform forces and the positions of concentrated force are taken as the output to construct the displacement field-boundary condition surrogate model.The prediction errors under the uniform loads of 1000N and 1600N are 0.15% and -0.48%, respectively, and the prediction errors under the load positions at 5mm and 10mm are 0.38% and -1.84%.The method based on data learning can provide new thinking for the prediction of structural static performance.

Key words: Data learning, Finite element analysis, Neural network, Performance prediction, Surrogate model

CLC Number: 

  • TP183
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