Determination of elastic loss of piezoelectric materials by impedance curve fitting using intelligent algorithms

Abstract

Understanding the loss parameters of piezoelectric materials is crucial for designing effective piezoelectric sensors. Traditional elastic loss parameter measurement techniques mainly rely on three methods: 3 dB bandwidth, impedance fitting, and ultrasonic attenuation. However, the elastic losses obtained through these methods are constant and frequency-independent, which does not align with the actual vibration characteristics of piezoelectric materials. Therefore, there is a need for a fast, accurate, and frequency-dependent method to obtain the elastic loss of piezoelectric materials. This paper introduces an approach that utilizes intelligent algorithms for fitting impedance curve to calculate elastic loss parameters. A frequency-dependent second-order energy loss model for piezoelectric materials is established. Then, a genetic algorithm is introduced to obtain the optimal elastic loss parameters. The results demonstrate a high consistency between theoretical and experimental impedances, with an error less than 5%. The elastic loss parameters obtained through intelligent algorithm-based impedance curve fitting match well with stress experiment results, with an error less than 6%. This method provides a rapid, accurate, and cost-effective way to obtain frequency-dependent second-order elastic loss parameters for piezoelectric materials.