WANG Bangxiang, LU Jingui, WANG Jingtao and et al. Application of neural network approximate model in lightweight design of hydraulic support top beam[J]. Journal of Light Industry, 2018, 33(2): 87-94. doi: 10.3969/j.issn.2096-1553.2018.02.013
Citation:
WANG Bangxiang, LU Jingui, WANG Jingtao and et al. Application of neural network approximate model in lightweight design of hydraulic support top beam[J]. Journal of Light Industry, 2018, 33(2): 87-94.
doi:
10.3969/j.issn.2096-1553.2018.02.013
Application of neural network approximate model in lightweight design of hydraulic support top beam
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Received Date:
2017-07-20
Available Online:
2018-03-15
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Abstract
For the top beam of hydraulic support to meet the requirements of working conditions, the goal of the project is to minimize the quality.A lightweight design method for the top beam was proposed combined with neural network approximation model and genetic algorithm.Firstly, using ANSYS to build a parametric model to the top beam, taking the quality of the top beam as the objective function,5 design variables that affect quality and intensity were selected,the optimization model of the top beam was established.Then, Optimal Latin Hypercube sampling method and ANSYS were used to get training samples.Neural network was applied to nonlinear fitting of sample set, and the approximate model of neural network was established.The approximate model was used to approximate the quality and maximal stress of the top beam,genetic algorithm was applied to solve the optimization model of the top beam, and the optimal solution was obtained finally.The optimization results showed that the quality of the top beam was 8 038.2 kg, which reduced by 9.66%.The maximum stress value was less than the yield strength of the top beam material and met the fatigue life requirement.
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