Accelerated optimizations of an electromagnetic acoustic transducer with artificial neural networks as metamodels

Abstract

Abstract. Electromagnetic acoustic transducers (EMATs) are noncontact transducers generating ultrasonic waves directly in the conductive sample. Despite the advantages, their transduction efficiencies are relatively low, so it is imperative to build accurate multiphysics models of EMATs and optimize the structural parameters accordingly, using a suitable optimization algorithm. The optimizing process often involves a large number of runs of the computationally expensive numerical models, so metamodels as substitutes for the real numerical models are helpful for the optimizations. In this work the focus is on the artificial neural networks as the metamodels of an omnidirectional EMAT, including the multilayer feedforward networks trained with the basic and improved back propagation algorithms and the radial basis function networks with exact and nonexact interpolations. The developed neural-network programs are tested on an example problem. Then the model of an omnidirectional EMAT generating Lamb waves in a linearized steel plate is introduced, and various approaches to calculate the amplitudes of the displacement component waveforms are discussed. The neural-network metamodels are then built for the EMAT model and compared to the displacement component amplitude (or ratio of amplitudes) surface data on a discrete grid of the design variables as the reference, applying a multifrequency model with FFT (fast Fourier transform)/IFFT (inverse FFT) processing. Finally the two-objective optimization problem is formulated with one objective function minimizing the ratio of the amplitude of the S0-mode Lamb wave to that of the A0 mode, and the other objective function minimizing as the negative amplitude of the A0 mode. Pareto fronts in the criterion space are solved with the neural-network models and the total time consumption is greatly decreased. From the study it could be observed that the radial basis function network with exact interpolation has the best performance considering its accuracy of approximation and the time required to build the metamodel.

Study of the relationship between the parameters of electromagnetic-acoustic transformation and the stress-strain state of the metal to solve the problem of remote monitoring of power equipment

“…Existing electromagnetic-acoustic diagnostic tools, due to the double mutual conversion of electromagnetic and acoustic waves, are significantly inferior in sensitivity and accuracy to acoustic tools with traditional contact piezoelectric transducers. A literature review revealed many publications of research results aimed at increasing the sensitivity and information content of the electromagnetic-acoustic monitoring method [12][13][14][15]. Of particular interest are research and development on non-contact testing of extended and large-sized metal structures by scanning them with acoustic waves generated by electromagnetic-acoustic transducers [16].…”

Neural Network Processing of Electromagnetic Acoustic Signals to Identify the Stress-Strain State and Damage of Power Equipment

“…Обратная задача -идентификация напряженно-деформированного состояния и уровня поврежденности структуры металла оборудования по значениям параметров гармонических составляющих сигнала ЭМАП может быть решена применением метода спектрального анализа». Но из-за большого объема исходной информации, которую необходимо при этом обработать, без использования современных интеллектуальных нейросетевых технологий это сделать практически невозможно, учитывая опыт подобных исследований, схожих по количеству обрабатываемых данных [7,8,12,[17][18][19][20].…”