Fotouhi, S. , Zhang, Z., He, M., Hao, Y., Giles-Donovan, N., Giuseppe Fenu, N. , Liu, B. and Cochran, S. (2023) Full-property Measurement for Piezoelectric Devices through Applied Artificial Intelligence. UIA51: Ultrasonic Industry Association, Utrecht, The Netherlands, 24-26 Apr 2023.
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Abstract
The piezoelectric effect is the most common means to excite power ultrasonics tools. However, the standard characterisation of piezoelectric materials is costly and complicated, particularly because of the need for multiple samples supporting only one or two modes each, with different poling requirements. These issues are of increasing importance because of the growing need for new piezoelectric materials, for example, lead-free. Moreover, the requirement for highly efficient finite element analysis (FEA) to design these tools demands precise, cheaper and easier material characterisation methods. This paper presents progress towards an innovative, simple characterisation approach. It is based on implementing machine learning-assisted global optimisation combined with FEA, using a single electrical impedance spectroscopy (EIS) measurement on a miniature sample. This solution may lead not only to considerable savings on costly material samples but also to save on experimental equipment and the training of engineers and materials scientists to use it. The technique also avoids difficulty in poling as no materials with large thicknesses are needed. Our approach is based on using an optimisation algorithm to fit an experimental EIS measurement with FEA. So far, surrogate model-assisted differential evolution for antenna synthesis (SADEA) and Levenberg-Marquardt (LM) algorithms have been studied for [001]-poled PZ54 (CTS Ferroperm, Kvistgaard, Denmark) as a reference, with SADEA showing the best results. The results are promising but there is still a need to achieve a better fit between the FEA and measurements and this is under investigation, via improvements in the optimisation process and implementation of complex piezoelectric material properties.
| Item Type: | Conference or Workshop Item |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Cochran, Professor Sandy and Fotouhi, Dr Sakineh and Hao, Yijia and Fenu, Dr Nicola and Liu, Professor Bo |
| Authors: | Fotouhi, S., Zhang, Z., He, M., Hao, Y., Giles-Donovan, N., Giuseppe Fenu, N., Liu, B., and Cochran, S. |
| College/School: | College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity College of Science and Engineering > School of Engineering > Systems Power and Energy |
| Research Group: | Centre for Medical and Industrial Ultrasonics (C-MIU) |
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