Abstract—With the rapid development of machine learning, it is possible to use neural networks to build models to predict performance of Ceramic Matrix Composites (CMCs) with raw materials and environments. In the traditional material science engineering, it always took a long time to develop a new CMC. Furthermore, there is still no theoretical basis providing references to design experiments to develop CMCs with ideal performances. This work proposed a model to predict the bending strength of CMCs with a Convolution Neural Network (CNN) using 8 factors considered to affect the bending strength of CMCs mainly. For the data were all collected from papers published on journals and conferences, and there is no standard to describe an experiment, the incompleteness of data influences the performance of our model seriously. Then we tried several methods to fill the data, finally the regression imputation with a dual-hidden-layer neural network performed a significant improvement of the CNN bending strength prediction model.
Index Terms—Bending strength, Ceramic Matrix Composites (CMC), Convolution Neural Network (CNN), imputation.
Gao Xiang and Li Guanghui are with the School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi 710129 China (e-mail: gaoxg@nwpu.edu.cn, sxlllslgh@mail.nwpu.edu.cn).
Tan Rong is with the School of Software, Northwestern Polytechnical University, Xi’an, Shaanxi 710129 China (e-mail: 2660364434@qq.com).
Yao Leijiang is with the School of Laboratory of Science and Technology onUAV, Northwestern Polytechnical University, Xi’an 710072 China (e-mail: yaolj@nwpu.edu.cn).
Cite: Gao Xiang, Li Guanghui, Tan Rong, and Yao Leijiang, "Research on Predicting the Bending Strength of Ceramic Matrix Composites with Process of Incomplete Data," International Journal of Machine Learning and Computing vol. 11, no. 3, pp. 224-229, 2021.
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).