International Journal of Applied Electromagnetics and Mechanics - Volume 73, issue 4

Authors: Di Barba, Paolo | Dughiero, Fabrizio | Forzan, Michele | Lowther, David A. | Marconi, Antonio | Mognaschi, Maria Evelina | Sykulski, Jan K.

Article Type: Research Article

Abstract: The authors explore the possibility of applying a convolutional Naeural Network (CNN) to the solution of coupled electromagnetic and thermal problem, focusing on the classical problem of induction heating systems, traditionally solved by resorting to Finite Element (FE) models. In fact, FE modelling is widely used in the design of induction heating systems due its accuracy, even if the solution of a coupled nonlinear problem is expensive in terms of computational time and hardware resources, notably in 3D analysis. A model based on CNN could be an interesting alternative; in fact, CNN is a learning model selected for its …excellent ability to converge, even when trained with a limited dataset. CNNs are able to treat images as input and they are used here as follows: given a temperature map in the workpiece, identify the corresponding vector of current, frequency and process heating time; this mapping is a model of the inverse induction heating problem. Specifically, we consider as an example the induction heating of a cylindrical steel billet, made of C45 steel, placed in a solenoidal inductor coil exhibiting the same axial length of the billet (TEAM 36 problem). A thorough heating process is usually applied before hot working of the billet, as in an extrusion process, but this methodology can be applied also in the design of induction hardening processes. First, a CNN has been trained from scratch by means of a dataset of FE solutions of coupled electromagnetic and thermal problems. For the sake of a comparison, a transfer learning technique is applied using GoogLeNet, i.e. a Deep Convolutional Neural Network able to classify images: starting from the pre-trained GoogLeNet, its training has been subsequently refined with the dataset of solutions from FE analyses. When the training dataset contains a limited number of samples, GoogleNet shows good accuracy in predicting the process parameters; in the case of a high number of samples in the training set, namely beyond a threshold like e.g. 1500, both CNNs show good accuracy of the result. Show more

Keywords: Numerical modelling, coupled fields, neural network, induction heating, finite-element analysis

DOI: 10.3233/JAE-230087

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. 73, no. 4, pp. 389-398, 2023

Authors: Ma, Yangyang | Li, Yongjian | Chen, Ruiying | Yue, Shuaichao | Sun, He

Article Type: Research Article

Abstract: With the increase in power electronic equipment in power system, the excitation of ferromagnetic materials often involves a large number of harmonics. Therefore, it is necessary to construct an accurate dynamic hysteresis model to adapt to this complicated operating state of electrical equipment. In this paper, a Hybrid Dynamic Hysteresis Model (HDHM), which can effectively characterize the harmonic excitation of materials is studied based on the Preisach model and Stacked Auto-Encoder (SAE) model. The static part of this model takes the form of the inverse Preisach model. And the Multiple Dynamic Hysteresis Model Set (MDHMS) is constructed by multiple dynamic …models of eddy currents and excess characteristics of the ferromagnetic materials. The dynamic part of the HDHM takes the form of the model structure combining the Stacked Auto-encoder and the MDHMS. The calculation results of the hysteresis loop and ferromagnetic loss in the harmonic condition of silicon steel sheet proves the validity of this model. Moreover, compared with the conventional dynamic hysteresis model, the HDHM has better accuracy and generalization ability. Show more

Keywords: Hybrid dynamic hysteresis model, the Preisach model, multiple-model set theory, ferromagnetic materials, iron loss

DOI: 10.3233/JAE-220112

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. 73, no. 4, pp. 399-413, 2023

Authors: Mušeljić, Eniz | Roppert, Klaus | Domenig, Lukas Daniel | Köstinger, Alice Reinbacher | Kaltenbacher, Manfred

Article Type: Research Article

Abstract: This paper is about the parameter identification of an energy based hysteresis model from measurements by employing automatic differentiation and neural networks. We first introduce the energy based hysteresis model and the parameters which are to be identified. Then we show how the model can benefit from automatic differentiation. After that we incorporate a parametrization of the energy based hysteresis model via distribution functions and identify the parameters of the distribution function. Then, the hysteresis model is sampled and the generated datasets are used to train neural networks to predict the hysteresis parameters. The described methods are tested and verified …on synthetic as well as measurement data. Show more

Keywords: Optimization; parameter identification; hysteresis; machine learning; neural networks

DOI: 10.3233/JAE-230107

Citation: International Journal of Applied Electromagnetics and Mechanics, vol. 73, no. 4, pp. 415-427, 2023