Design, analysis, and optimization of a magnetoelectric actuator using regression modeling, numerical simulation and metaheuristics algorithm
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In this study, a new method was proposed for the design of composite magnetoelectric actuator. Design of experiment (DOE) was utilized to investigate the mutual effect of geometric parameters. Moreover, the effect of impedance phase angle, magnetic field, and bias field were studied through finite element (FE) modeling. Resonance frequency, displacement value, magnetoelectric coefficient, and mode shape were considered as response variables. Analysis of variance (ANOVA), regression modeling and response surface method (RSM) were used to investigate the pair-wise effect of input parameters on the response variables. ANOVA results showed that the magnetoelectric length and piezoelectric thickness are the most important parameters affecting the magnetoelectric performance. The optimization process was performed using Metaheuristics algorithm. Optimum results were verified using magnetoelectric measurement setup and Laser Doppler Vibrometry device. The accuracy of the FE model in resonance frequency prediction was estimated at 97%. The prediction error of the FE model for the magnetoelectric voltage parameter was 14.6%, which was about 12.9% better than the regression model. The confirmation test showed that the regression modeling can only predict magnetoelectric behavior and for determining magnetoelectric performance, a precise FE model would be more reliable. Such proposed optimization technique can be used in the design of magnetoelectric composites.
The authors would like to thank Prof. Yumei Wen and Prof. Ping Li (Lab of sensor and Instrument system, Electronic Department, Shanghai Jiao Tong University, Shanghai, China) for their technical support in manufacturing, characterization, and measurements of the magnetoelectric samples.
- 5.W. Huang et al., “Ferroelectric domain switching dynamics and memristive behaviors in BiFeO3-based magnetoelectric heterojunctions,” Journal of Physics D: Applied Physics, vol. 51, no. 23, p. 234005, 2018/05/17 2018Google Scholar
- 14.A.-P. Wang et al., Influence of composition ratio on ferroelectric, magnetic and magnetoelectric properties of PMN–PT/CFO composite thin films. J. Mater. Sci.: Mater. Electron. 29(12), 10164–10169 (2018)Google Scholar
- 17.L. Chen, P. Li, Y.M. Wen, Y. Zhu, Tunable characteristics of bending resonance frequency in magnetoelectric laminated composites. Chin. Phys. B 22(7), 1–5 (2013)Google Scholar
- 31.K. Krishnaiah, P. Shahabudeen, Applied Design OF Experiments and Taguchi Methods (PHI Learning, New Delhi, 2012)Google Scholar
- 32.M. Kaltenbacher, Numerical Simulation of Mechatronic Sensors and Actuators (Springer, Berlin Heidelberg, 2013)Google Scholar
- 33.Apc International, Piezoelectric Ceramics: Principles and Applications (APC International, Mackeyville, 2011)Google Scholar
- 34.S. Chikazumi, C.D. Graham, Physics of Ferromagnetism 2e (International Series of Monographs on Physics) (OUP Oxford, Oxford, 2009)Google Scholar
- 35.R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley Series in Probability and Statistics) (Wiley, New York, 2011)Google Scholar
- 36.K. Najim, E. Ikonen, A.K. Daoud, Stochastic Processes: Estimation (Elsevier Science, Optimisation and Analysis, 2004)Google Scholar