Material properties identification of a piezoelectric beam using inverse method

Author:

Nematollahi Mohammad Amin1ORCID,Hasanshahi Behzad2,Eftekhari Malihe3,Safavi Ali Akbar4

Affiliation:

1. Department of Biosystems Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

2. Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada

3. Department of Mechanical Engineering, Yazd University, Yazd, Iran

4. Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

Abstract

This paper presents an inverse method for material properties identification of a piezoelectric beam (piezoelectric charge and relative dielectric coefficients) using a wavelet-based neural network as an inverse tool. The identification analysis is carried out by using two approaches. In the first approach, i.e. sensor mode analysis, the input data for wavelet-based neural network training are measured voltages at several specific points on the beam's top surface resulting from the applied beam tip deflection. In the second approach, i.e. actuation mode analysis, the input data are values of the beam tip deflection caused by applying voltage on the beam's top surface. In this study, the input parameters employed to train the wavelet-based neural network are obtained using the finite element method. The identification results are compared with those of some conventional neural networks including radial basis function and multilayer perceptron. The results show that the proposed neural network is an efficient tool in the material properties identification problem.

Publisher

SAGE Publications

Subject

Mechanical Engineering

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Hysteresis modelling and compensation for piezoelectric actuator using Jaya-BP neural network;Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science;2021-09-16

2. An inverse meshfree thermoelastic analysis for identification of temperature-dependent thermal and mechanical material properties;Journal of Thermal Stresses;2020-06-19

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