Machine Learning Identification of Piezoelectric Properties

Abstract

The behavior of a piezoelectric element can be reproduced with high accuracy using numerical simulations. However, simulations are limited by knowledge of the parameters in the piezoelectric model. The identification of the piezoelectric model can be addressed using different techniques but is still a problem for manufacturers and end users. In this paper, we present the use of a machine learning approach to determine the parameters in the model. In this first work, the main sensitive parameters, c11, c13, c33, c44 and e33 were predicted using a neural network numerically trained by using finite element simulations. Close to one million simulations were performed by changing the value of the selected parameters by ±10% around the starting point. To train the network, the values of a PZT 27 piezoelectric ceramic with a diameter of 20 mm and thickness of 2 mm were used as the initial seed. The first results were very encouraging, and provided the original parameters with a difference of less than 0.6% in the worst case. The proposed approach is extremely fast after the training of the neural network. It is suitable for manufacturers or end users that work with the same material and a fixed number of geometries.