Year: 2024
Type: article
Abstract: Machine learning is a powerful tool that can predict the iron loss of electromagnetic steels with high accuracy. However, there is no theoretical explanation as to why machine learning can make such accurate predictions; it is a black box. Therefore, it is unclear how to select an algorithm for predicting iron loss behaviors among many machine learning algorithms available. In this study, we used approximately 10000 iron loss data for electromagnetic steels under stress and 19 machine learning algorithms to evaluate and compare the learning algorithms in terms of prediction accuracy, robustness, the number of data required, avoidance of overfitting, and agreement with experimental results. As a result, we conclude that LightGBM is the best algorithm for predicting the iron loss properties of electromagnetic steels. Although the discussion in this study has not yet led to a theoretical breakthrough in the field of machine learning, we hope that it will provide an effective guideline for selecting the learning algorithm in materials science.
Authors: Kyouhei Hayakawa, Isao Matsui, Takumi Hamaguchi, Takaharu Maeguchi
Institution: Mitsubishi Heavy Industries (Japan)
Language: English
Field: Materials Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404667013