基于应变模态振型和误差补偿的形状重构方法

Shape reconstruction method based on strain mode shape and errorcompensation

摘要:

形状传感技术是近年来传感领域备受关注的新研究方向,具有广泛的应用前景。本文提出了基于应变模态振型和误差补偿的形状重构方法,通过测量物体部分位置点的应变数据,采用模态理论实现应变-位移转换,进而重构物体形状。本文以长宽高分别为1000mm、1000mm和0.5mm的钛合金板作为研究对象,通过ANSYS workbench18有限元仿真软件获取位移模态振型及应变模态振型,依据有限元仿真中位置点模态的相似性,采用K-means++聚类算法对应变测量点位置进行优化,在合金板上表面施加400N的力使其产生形变。本文算法的形状重构误差小于常规的均匀分布算法。采用径向基函数神经网络(Radial Basis Function Neural Network,RBFNN)对误差和重构位移数据集进行了训练,依据重构位移预测误差,其拟合误差不大于3.5%。

Abstract:

Shape sensing technology is a new research direction in recent years and has a wide range of application prospects. In this paper, a shape reconstruction method based on strain mode mode and error compensation is proposed. By Measuring the strain data of some position points of the object, the modal theory is used to realize the strain-displacement transformation, and then the shape of the object is reconstructed. In this paper, titanium alloy plates with length, width and height of 1000mm, 1000mm and 0.5mm were taken as the research object. The displacement mode and strain mode modes were obtained by ANSYS workbench 18 finite element simulation software. According to the modal similarity of position points in the finite element simulation, The K-means++ clustering algorithm was used to optimize the position of strain measuring points, and the strain deformation was generated by applying 400N force on the upper surface of the alloy plate. The shape reconstruction error of the proposed algorithm is smaller than that of the conventional uniform distribution algorithm. A Radial Basis Function Neural Network (RBFNN) was used to train the error and reconstructed displacement data set. According to the reconstructed displacement prediction error, the fitting error was less than 3.5%.