An intelligent measurement method of the resonant frequency of ultrasonic scalpel transducers based on PSO-BP neural network. (28th February 2022)

• Record Type: Journal Article
• Authors: Gan, Yi; Meng, Bin; Chen, Yazhou; Sun, Fujia
• Is Part Of: Measurement. Volume 190(2022)
• Publication Date: 2022-02-28
• Subjects: Ultrasonic scalpel transducer -- Resonant frequency -- Particle swarm optimization -- BP neural network -- Intelligent measurement; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110680
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: The feature variables that affect the resonant frequency are found. It is feasible to evaluate resonance frequency by neural network method. PSO-BP neural network is faster and more accurate than traditional methods. Neural network method can reduce the cost of frequency measurement. Abstract: In the laboratory, using traditional methods to measure the resonant frequency of transducers is inefficient and costly. For this, in this study, the feature variables determining the resonant frequency of transducer was firstly analyzed with the equivalent circuit method. Based on the input–output mapping function of back propagation (BP) neural network, the nonlinear relationship model between feature variables and the resonant frequency was established. Then, particle swarm optimization (PSO) was used to optimize the initial weight and threshold of BP neural network to obtain the best parameter combination, thereby improving the output accuracy of BP neural network. The experimental data showed that it is effective to measure the resonant frequency of the transducer by PSO-BP neural network, and the measurement error was 1 Hz. Compared to traditional measurement method, the intelligent method has the advantages of low cost, high efficiency, fast response speed, and strong robustness.