Parametric identification of the mathematical model of the micro-arc oxidation process

Anatoliy Semenov; Ekaterina Pecherskaya; Pavel Golubkov; Sergey Gurin; Dmitriy Artamonov; Yuliya Shepeleva

doi:10.1016/j.heliyon.2023.e19995

Parametric identification of the mathematical model of the micro-arc oxidation process

Heliyon. 2023 Sep 10;9(9):e19995. doi: 10.1016/j.heliyon.2023.e19995. eCollection 2023 Sep.

Authors

Anatoliy Semenov¹, Ekaterina Pecherskaya¹, Pavel Golubkov¹, Sergey Gurin¹, Dmitriy Artamonov², Yuliya Shepeleva²

Affiliations

¹ Department of Information and Measurement Equipment and Metrology, Penza State University, Krasnaya Street 40, Penza 440026, Russia.
² Polytechnic Institute, Penza State University, Krasnaya Street 40, Penza 440026, Russia.

Abstract

The article is aimed at solving the problem of parametric identification of non-linear object models using the example of a mathematical model of the micro-arc oxidation process. An algorithm for parametric identification, based on an experiment in the micro-arc oxidation process, the results of which form a training and control sample is proposed; sequential training of neural networks and calculation of the parameters estimates of the nonlinear model according to experimental data are performed. Experimental testing of the proposed method of neural network parametric identification on the example of the micro-arc oxidation process confirmed that the standard deviation of current and voltage from the nominal values does not exceed ±4%. The obtained results were used in the development of an intelligent hardware-software complex for the production of protective coatings by the micro-arc oxidation method.

Keywords: Micro-arc oxidation; Neural networks; Non-linear models; Parametric identification; Reservoir computing.