Searched for: subject%3A%22Small%255C+sample%255C+problem%22
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Wei, Xiaolu (author), van der Zwaag, S. (author), Jia, Zixi (author), Wang, Chenchong (author), Xu, W. (author)
In this research a machine learning model for predicting the rotating bending fatigue strength and the high-throughput design of fatigue resistant steels is proposed. In this transfer prediction framework, machine learning models are first trained to estimate tensile properties (yield strength, tensile strength and elongation) on the basis of...
journal article 2022
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Shen, Chunguang (author), Wang, Chenchong (author), Huang, Minghao (author), Xu, Ning (author), van der Zwaag, S. (author), Xu, W. (author)
We present an electron backscattered diffraction (EBSD)-trained deep learning (DL) method integrating traditional material characterization informatics and artificial intelligence for a more accurate classification and quantification of complex microstructures using only regular scanning electron microscope (SEM) images. In this method, EBSD...
journal article 2021
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Shen, Chunguang (author), Wang, Chenchong (author), Wei, Xiaolu (author), Li, Yong (author), van der Zwaag, S. (author), Xu, W. (author)
With the development of the materials genome philosophy and data mining methodologies, machine learning (ML) has been widely applied for discovering new materials in various systems including high-end steels with improved performance. Although recently, some attempts have been made to incorporate physical features in the ML process, its...
journal article 2019