MULTI-OBJECTIVE ROBUST OPTIMIZATION DESIGN OF COMPLIANT HINGE BASED ON BP NEURAL NETWORK (MT)

WU JianJun; LI JiaHui

doi:10.16579/j.issn.1001.9669.2023.04.014

您当前的位置：

首页 >

文章列表页 >

MULTI-OBJECTIVE ROBUST OPTIMIZATION DESIGN OF COMPLIANT HINGE BASED ON BP NEURAL NETWORK (MT)

更新时间：2023-09-25

- MULTI-OBJECTIVE ROBUST OPTIMIZATION DESIGN OF COMPLIANT HINGE BASED ON BP NEURAL NETWORK (MT)
- Journal of Mechanical Strength Issue 4, Pages: 856-861(2023)
- 作者机构：
  
  1. 江西理工大学机电工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2023.04.014
  CLC：
扫描看全文
WU JianJun, LI JiaHui. MULTI-OBJECTIVE ROBUST OPTIMIZATION DESIGN OF COMPLIANT HINGE BASED ON BP NEURAL NETWORK (MT). [J]. Journal of Mechanical Strength (4):856-861(2023)
DOI：

WU JianJun, LI JiaHui. MULTI-OBJECTIVE ROBUST OPTIMIZATION DESIGN OF COMPLIANT HINGE BASED ON BP NEURAL NETWORK (MT). [J]. Journal of Mechanical Strength (4):856-861(2023) DOI： 10.16579/j.issn.1001.9669.2023.04.014.

摘要

为了提高柔顺铰链的稳健性，引入遗传算法和反向传播(Back Propagation, BP)神经网络方法对柔顺机构进行参数优化，运用正交试验来选出训练参数和测试参数，建立BP神经网络模型，利用神经网络的非线性拟合能力和遗传算法的全局搜索寻优能力对柔度和固有频率信噪比分别进行单目标和多目标寻找选取范围内的全局最优，不仅仅局限于选取因素水平的排列组合，也为提高柔顺铰链稳健性提供了一种新的解决途径。实验结果显示，柔顺铰链综合评价函数更优，实现了稳健优化设计的目的，证明了该方法的有效性。

Abstract

In order to improve the robustness of the compliant hinge, genetic algorithm and BP neural network methods are introduced to optimize the parameters of the compliant mechanism. Orthogonal experiments are used to select training parameters and test parameters, a BP neural network model is established, and by using the nonlinear fitting ability of the neural network and the global search and optimization ability of genetic algorithm. The flexibility and natural frequency signal-to-noise ratio are used to find the global optimum in the selection range for single and multi-objectives. It is not only limited to the permutation and combination of selected factor levels, but also provides a new solution to improve the compliance hinge robustness. The experimental results show that the comprehensive evaluation function of the compliant hinge is better, which achieves the purpose of robust optimization design, and proves the effectiveness of the method.

关键词

柔顺机构柔度BP神经网络稳健优化遗传算法

Keywords

Compliant mechanismFlexibilityBP neural networkRobust optimizationGenetic algorithm

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

RESEARCH ON PERFORMANCE PREDICTION OF THIN SEAM SHEARER BY COMBINING GENETIC ALGORITHM WITH BP NEURAL NETWORK

MULTI-OBJECTIVE RELIABILITY OPTIMIZATION DESIGN OF BOGIE FRAME BASED ON GA WEIGHTED NETWORK

STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK

FATIGUE LIFE ANALYSIS AND PREDICTION OF PLANET CARRIER IN CUTTING PART OF SHEARER BASED ON IMPROVED PSO-BP

OPTIMIZATION OF BLADE DEFLECTION BASED ON HYBRID GENETIC ALGORITHM

Related Author

No data

Related Institution

College of Mechanical Engineering,Liaoning Technical University

Jiaozuo Normal College

Yangtze Delta Region Institute(Huzhou),University of Electronic Science and Technology of China

Institute of Electronic and Information Engineering in Guangdong, University of Electronic Science and Technology of China

School of Locomotive and Rolling Stock Engineering,Dalian Jiaotong University

⁰