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

A 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.