Sound waves are a form of energy transmission in the mechanical vibration state of an object. Ultrasound is a type of sound wave with a vibration frequency higher than 20 kHz, which cannot be heard by humans. A piezoelectric ultrasonic transducer (PUT) is a device for achieving mutual conversion of mechanical energy and electrical energy. Due to the advantages of high safety and low cost, a PUT has been widely used as the core device for non-destructive testing (NDT), medical imaging, particle manipulation, and flow measurement. The traditional optimization design methods are mainly based on an analytical model, an equivalent circuit model, or a finite element model and the design parameters are adjusted by a trial-and-error method, which relies on the experience of experts and has a relatively low efficiency. Recently, by combining intelligent optimization algorithms, efficient optimization design methods for a PUT have been developed based on a traditional model or a data-driven model, which can effectively improve the design efficiency of a PUT and reduce its development cycle and cost.
Due to the advantages of high safety and low cost, a PUT has been widely used as the core device for non-destructive testing (NDT), medical imaging, particle manipulation, and flow measurement [3][4][5][6]. In NDT, ultrasonic detection and the guided-wave structural health monitoring (SHM) method are combined to detect the damage of wind turbine blades, and the defect position can be precisely determined [7]. The annular PUT array printed by three-dimensional (3D) printing technology has adjustable focal domain and resolution and has been successfully used for NDT [8]. In medical imaging, the piezoelectric performance of a PUT is improved by polarizing to achieve high imaging resolution [9]. In addition, the inaccuracy of transcranial imaging caused by phase aberration can be eliminated by using ultrasonic adaptive beam forming to make ultrasonic imaging more accurate [10]. An acoustic radiation force optical coherence tomography system, using integrated micro ultrasound and optical coherence tomography, can map the correlated elasticity of vascular tissue to aid medical diagnosis [11]. In particle manipulation, the accurate control of ultrasound is extremely important for non-contact manipulation of biologics and bioanalysis [12]. Single-beam acoustic tweezers with pressure-focusing technology can be used as the manipulators in a wide range of biomedical and chemical sciences [13]. In addition, a bending PUT has been widely used for flow measurement under atmospheric pressure conditions [14]. A flexible piezocomposite ultrasound transducer can continuously measure blood pressure by tracking the ultrasonic motion of the vessel wall, and it can be used for non-invasive, non-obstructive, calibration-free blood pressure and blood flow monitoring [15]. Therefore, a PUT is the key component in specific ultrasonic application systems, and its performance mainly affects the performance and effectiveness of these systems.
Using CIVA software, Puel et al. [16] constructed a multiobjective optimization function for the optimization design of a PUT, and the evolutionary algorithm was used to optimize the design parameters, as shown in
Figure 1A. According to the required performance of detection, the CF, size, and kerf of element could be optimized by the developed method. According to the principle of sound field, Choi et al. [17] proposed a mathematical model to describe the acoustic field distribution, as shown in
Figure 1B. 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. [18] developed an optimization design method for PUT (as shown in
Figure 1C). According to the optimized design parameters, the fabricated PUT had a CF of 6.3 MHz and a −6 dB bandwidth of 68.25%. With the combination of topology optimization and a FEM, Rubio et al. [19] designed a functional gradient PUT, (
Figure 1
Figure 1.
A) Evolutionary algorithm-based optimization design method for a piezoelectric ultrasonic transducer (reproduced from [16]); (
B) mathematical model for a concave annular high intensity focused ultrasonic transducer and its finite element model (reproduced from [16]); (
C) optimization design for a piezoelectric ultrasonic transducer using the particle swarm optimization algorithm (reproduced from [17]); (
D) functionally graded piezoelectric ultrasonic transducer optimized by using the topological optimization algorithm (reproduced from [18]).
On the basis of a genetic algorithm-based back-propagation neural network (GABPNN) model and the PSO algorithm, Chen et al. [20] proposed an optimization design method for a high-performance transmitting PUT (shown in
Figure 2A). The GABPNN models were trained using the data simulated by PiezoCAD software to describe the relationships among the design and performance parameters of the PUT, and the PSO algorithm was adopted to determine the design parameters. The CF and insertion loss of the designed PUT were 1.07 MHz and 7.2 dB, respectively, which had excellent transmitting performance. In addition, Chen et al. [21] designed a 1-3 piezocomposite ultrasonic transducer for imaging application based on a data-driven model and modified PSO algorithm. As shown in
Figure 2B, the ultrasonic images showed that the designed 1-3 piezocomposite ultrasonic transducer had high resolution and good signal-to-noise ratio. On the basis of intelligent optimization algorithms, Li et al. [22] designed a PUT with multi-match layers, as shown in
Figure 2
Figure 2.
A) Optimization design method for a high-performance transmitting piezoelectric ultrasonic transducer (reproduced from [20]); (
B) designed and fabricated 1-3 piezocomposite ultrasonic transducer for ultrasonic imaging (reproduced from [20]); (
C) optimization design method for a piezoelectric ultrasonic transducer with multi-match layers and testing results (reproduced from [21]).
In order to effectively and flexibly control the acoustic field, Li et al. [23] proposed an efficient optimization design method for an acoustic liquid lens (as shown in
Figure 3
Figure 3B, Sun et al. [24] proposed an intelligent optimization design method for matching layers of a PUT. The neural network models were trained to establish the mapping relationships among the thicknesses of matching layers and performance parameters of the PUT. An improved PSO algorithm was used to optimize the thicknesses of matching layers (Ag-epoxy and parylene C). It was found that the PUT with the optimized matching layers had a −6 dB bandwidth of 68.5% and pulse echo width of 0.123 us, which were superior to those of the PUT with matching layers determined by the traditional quarter-wavelength theory.
Figure 3.
A) Acoustic liquid lens and ultrasonic imaging results of porcine eye (reproduced from [23]); (
B) Effect of matching layers on the performance of a piezoelectric ultrasonic transducer, and the intelligent optimization design of piezoelectric ultrasonic transducer with two matching layers (reproduced from [23]).