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Multi-parameters optimization for electromagnetic acoustic transducers using surrogate-assisted particle swarm optimizer

机译：使用代理辅助粒子群优化器的电磁声传感器多参数优化

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摘要

In ultrasonic testing field, the design and optimization of point-focusing shear vertical waves electromagnetic acoustic transducer (PFSV-EMAT) with high energy conversion efficiency have been a challenging task. In this work, a novel multi-parameter optimization method is described to improve PFSV-EMATs' conversion performance, which integrates the newly proposed hybrid surrogate modeling (HSM) approach and particle swarm optimization (PSO) algorithm for efficient optimization. Based on the established finite element model of EMATs, the amplitudes of the displacement components at the observation point of a plate is the objective function to be maximized with five parameters pertaining to the cylindrical magnets, coaxial meander-line coils, and excitation signal, as design variables. Then, a brand-new HSM-assisted PSO algorithm is proposed and the superiority and rationality of the HSM strategy are proven. In addition, the multi-parameter optimization of PFSV-EMATs is conducted with the proposed HSM-PSO algorithm. Finally, experiments are carried out to verify the validation of optimization results, indicating that the increased signal amplitude of 12 times can be achieved by the optimized PFSV-EMAT, which has the improved signal amplitude and consistent point-focusing behavior, compared with the non-optimized one.
机译：在超声波检测场中，具有高能量转换效率的点聚焦剪切垂直波的设计和优化是具有高能量转换效率的挑战性任务。在这项工作中，描述了一种新型的多参数优化方法来提高PFSV-EMATS的转换性能，这集成了新提议的混合代理建模（HSM）方法和粒子群优化（PSO）算法，以实现有效优化。基于EMAT的已建立的有限元模型，板的观察点处的位移部件的幅度是目标函数，其具有与圆柱形磁体，同轴曲线线圈和激励信号有关的五个参数。设计变量。然后，提出了一个全新的HSM辅助PSO算法，并证明了HSM策略的优越性和合理性。此外，使用所提出的HSM-PSO算法进行PFSV-Emats的多参数优化。最后，进行实验以验证优化结果的验证，表明，通过优化的PFSV-EMAT可以实现12次的增加的信号幅度，这与非活动相比具有改进的信号幅度和一致的点聚焦行为。 - 优化一个。

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