A comparative assessment of classification methods for resonance frequency prediction of Langevin piezoelectric transducers

Abstract

a b s t r a c tA Langevin piezoelectric transducer is used as a physical element for transmitting and receiving sound waves. The operating frequency of a transducer determines the distance that the sound wave can travel, so it is important to measure it. Due to the fact the structure of a transducer is quite complicated, it is quite difficult to estimate the precise physical parameters for the simulation model. Therefore, it takes a long time to measure the resonance frequency in the laboratory and fix the parameters by trial and error methods. This study applies a learning method to estimate a transducer frequency instead by trial and error experiments. The learning methods applied and compared including artificial neural network, support vector machine, C4.5, neuro-fuzzy, and ega-fuzzification. Compared with the theoretical one-dimensional model (simple lump element model), the results indicate that a learning method is an efficient way to estimate the piezoelectric transducer resonance frequency. The mega-fuzzification method is the best compared with other methods in this study.

Research on Inherent Frequency and Vibration Characteristics of Sandwich Piezoelectric Ceramic Transducer

“…[ 23 ] proposed a PSpice loss model for a sandwiched piezoelectric ultrasonic transducer undergoing longitudinal vibration using one-dimensional wave and transmission line theory to obtain the transducer’s resonance and anti-resonance frequencies. Chen [ 24 ] estimated the frequency of a sandwich transducer under various parameters by giant fuzzification-learning methods. Although the equivalent circuit method and matrix transfer method can model continuous systems with discrete components, which are valid around an inherent frequency, modeling the transducer’s continuous system vibration [ 25 ] can be valid in a wider range of frequencies.…”