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Communication

Rubber Material Property Prediction Using Electron Microscope Images of Internal Structures Taken under Multiple Conditions

1
Education and Research Center for Mathematical and Data Science, Hokkaido University, N-12, W-7, Kita-ku, Sapporo 060-0812, Japan
2
Department of Creative Engineering, National Institute of Technology, Kushiro College, Otanoshike-Nishi 2-32-1, Kushiro 084-0916, Japan
3
Office of Institutional Research, Hokkaido University, N-8, W-5, Kita-ku, Sapporo 060-0808, Japan
4
Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Jose Manuel Molina López
Sensors 2021, 21(6), 2088; https://doi.org/10.3390/s21062088
Received: 11 February 2021 / Revised: 10 March 2021 / Accepted: 10 March 2021 / Published: 16 March 2021
(This article belongs to the Section Electronic Sensors)
A method for prediction of properties of rubber materials utilizing electron microscope images of internal structures taken under multiple conditions is presented in this paper. Electron microscope images of rubber materials are taken under several conditions, and effective conditions for the prediction of properties are different for each rubber material. Novel approaches for the selection and integration of reliable prediction results are used in the proposed method. The proposed method enables selection of reliable results based on prediction intervals that can be derived by the predictors that are each constructed from electron microscope images taken under each condition. By monitoring the relationship between prediction results and prediction intervals derived from the corresponding predictors, it can be determined whether the target prediction results are reliable. Furthermore, the proposed method integrates the selected reliable results based on Dempster–Shafer (DS) evidence theory, and this integration result is regarded as a final prediction result. The DS evidence theory enables integration of multiple prediction results, even if the results are obtained from different imaging conditions. This means that integration can even be realized if electron microscope images of each material are taken under different conditions and even if these conditions are different for target materials. This nonconventional approach is suitable for our application, i.e., property prediction. Experiments on rubber material data showed that the evaluation index mean absolute percent error (MAPE) was under 10% by the proposed method. The performance of the proposed method outperformed conventional comparative property estimation methods. Consequently, the proposed method can realize accurate prediction of the properties with consideration of the characteristic of electron microscope images described above. View Full-Text
Keywords: rubber materials; property prediction; electron microscope images; Dempster–Shafer evidence theory rubber materials; property prediction; electron microscope images; Dempster–Shafer evidence theory
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MDPI and ACS Style

Togo, R.; Saito, N.; Maeda, K.; Ogawa, T.; Haseyama, M. Rubber Material Property Prediction Using Electron Microscope Images of Internal Structures Taken under Multiple Conditions. Sensors 2021, 21, 2088. https://doi.org/10.3390/s21062088

AMA Style

Togo R, Saito N, Maeda K, Ogawa T, Haseyama M. Rubber Material Property Prediction Using Electron Microscope Images of Internal Structures Taken under Multiple Conditions. Sensors. 2021; 21(6):2088. https://doi.org/10.3390/s21062088

Chicago/Turabian Style

Togo, Ren, Naoki Saito, Keisuke Maeda, Takahiro Ogawa, and Miki Haseyama. 2021. "Rubber Material Property Prediction Using Electron Microscope Images of Internal Structures Taken under Multiple Conditions" Sensors 21, no. 6: 2088. https://doi.org/10.3390/s21062088

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