现代制造工程 ›› 2018, Vol. 448 ›› Issue (1): 59-62.doi: 10.16731/j.cnki.1671-3133.2018.01.012

• 车辆工程制造技术 • 上一篇    下一篇

基于BP神经网络的挖掘机动臂应力预测

闫二乐1,2,林航1,林述温1,杨拴强3   

  1. 1 福州大学机械工程及其自动化学院,福州 350108;
    2 福建中海创自动化科技有限公司,福州 350108;
    3 福建江夏学院工程学院,福州 350108
  • 收稿日期:2016-10-25 出版日期:2018-01-20 发布日期:2018-07-24
  • 作者简介:闫二乐,硕士研究生,主要研究方向为智能优化设计,已经发表论文2篇。E-mail:260187357@qq.com
  • 基金资助:
    国家自然科学基金青年基金项目(51405085)

Stress prediction model of excavator boom based on BP neural network

Yan Erle1,2,Lin Hang1,Lin Shuwen1,Yang Shuanqiang3   

  1. 1 School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350108,China;
    2 Fujian Histron Automation Technology Co.Ltd., Fuzhou 350108,China;
    3 School of Engineering,Fujian Jiangxia University, Fuzhou 350108,China
  • Received:2016-10-25 Online:2018-01-20 Published:2018-07-24

摘要: 针对挖掘机动臂结构优化过程中需要反复调用ANSYS有限元软件,导致应力强度控制难度大、优化过程繁琐且效率低下的问题,提出一种基于BP神经网络预测动臂应力的方法。通过在挖掘机动臂结构优化设计软件中设定截面选取规则,选取应力普查的截面导出截面应力样本,并在MATLAB中运用BP神经网络建立挖掘机动臂应力预测模型。以中小型挖掘机动臂为例,建立基于动臂应力普查的约束,获取应力预测样本,运用建立的神经网络预测模型对动臂应力进行预测。结果表明,网络预测应力值与实验数据吻合且误差小于6.80 %,建立的预测模型能提高动臂结构的优化效率。

关键词: 挖掘机动臂, 应力普查, 应力特征截面, BP神经网络, 应力预测

Abstract: The finite element software ANSYS needs to be run repeatedly in the excavator boom structure optimization process,making the optimization process cumbersome and inefficient. To address this problem, an intelligent optimization model for four typical stress conditions of the excavator boom was proposed. Stress census section was determined through setting rules in optimal design software of excavator boom and establishing a stress prediction model for the excavator boom based on BP network. The small and medium excavator booms were used as examples and stress prediction models were established to improve the optimization efficiency of the boom structure. The results show that predict stress was in consistent with the experimental data with error less than 6.08 %.

Key words: excavator boom, stress census, stress characteristic section, BP neural network, stress prediction

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