Particle Swarm Optimization Algorithm-Based Design Method for Ultrasonic Transducers

Abstract

In order to improve the fabrication efficiency and performance of an ultrasonic transducer (UT), a particle swarm optimization (PSO) algorithm-based design method was established and combined with an electrically equivalent circuit model. The relationship between the design and performance parameters of the UT is described by an electrically equivalent circuit model. Optimality criteria were established according to the desired performance; then, the design parameters were iteratively optimized using a PSO algorithm. The Pb(ZrxTi1−x)O3 (PZT) ceramic UT was designed by the proposed method to verify its effectiveness. A center frequency of 6 MHz and a bandwidth of −6 dB (70%) were the desired performance characteristics. The optimized thicknesses of the piezoelectric and matching layers were 255 μm and 102 μm. The experimental results agree with those determined by the equivalent circuit model, and the center frequency and −6 dB bandwidth of the fabricated UT were 6.3 MHz and 68.25%, respectively, which verifies the effectiveness of the developed optimization design method.

Recent Development and Perspectives of Optimization Design Methods for Piezoelectric Ultrasonic Transducers

“…Then, the design parameters of a concave annular high intensity focused ultrasonic transducer could be optimized by using a nonlinear programming algorithm, which was beneficial for fabricating a high-intensity focused ultrasonic transducer. By combining an ECM and the particle swarm optimization (PSO) algorithm, Chen et al [ 49 ] developed an optimization design method for PUT (as shown in Figure 7 C). According to the optimized design parameters, the fabricated PUT had a CF of 6.3 MHz and a −6 dB bandwidth of 68.25%.…”

“… ( A ) Evolutionary algorithm-based optimization design method for a piezoelectric ultrasonic transducer (reproduced from [ 47 ]); ( B ) mathematical model for a concave annular high intensity focused ultrasonic transducer and its finite element model (reproduced from [ 47 ]); ( C ) optimization design for a piezoelectric ultrasonic transducer using the particle swarm optimization algorithm (reproduced from [ 48 ]); ( D ) functionally graded piezoelectric ultrasonic transducer optimized by using the topological optimization algorithm (reproduced from [ 49 ]). …”

Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage Using Modified Particle Swarm Optimization

“…Particle swarm optimization (PSO) is a widely used swarm-intelligence-based optimization method [ 18 , 19 , 20 , 21 , 22 ]. However, a major drawback of PSO is that it tends to get trapped in local optima and is, therefore, unable to find a global optimal solution.…”

Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships

“…Focusing on enhancement of the control performance, optimization techniques have been widely used and applied. They provide systematic procedure for designing of controllers, tuning the parameters and gains, and online estimation/calibration for unknown parameters caused by environmental changes or disturbances [21,28]. In the literature, various optimization algorithms have been developed to improve the quality of the closed-loop system such as Artificial Bee Colony (ABC) algorithm [29], Ant Colony Optimization (ACO) algorithm [30], Bacterial Foraging Optimization (BFO) algorithm [31], Multi-Verse Optimization (MVO) algorithm [32] and Gravitational Search Algorithm (GSA) [33].…”

“…In the literature, various optimization algorithms have been developed to improve the quality of the closed-loop system such as Artificial Bee Colony (ABC) algorithm [29], Ant Colony Optimization (ACO) algorithm [30], Bacterial Foraging Optimization (BFO) algorithm [31], Multi-Verse Optimization (MVO) algorithm [32] and Gravitational Search Algorithm (GSA) [33]. Particle Swarm Optimization (PSO) is one of the effective optimization methods that has been successfully used in tuning PID controllers as in [34], for quadrotor stabilization based on integral backstepping control design [35], for tuning the parameters of fuzzy PD controller with minimization of integral square error cost function in [36], to improve the performance of an ultrasonic transducer [28]. For the control of DP systems, authors in [21] have deployed the PSO method to optimize the fuzzy function/parameters to enhance the quality and performance of the DP system of the ship.…”