Xinyu Liu, Bo Wen, Xinhua Wang, Qiang Niu, and Hong Chen, Prediction of Hot Ductility of Low-Carbon Steels Based on BP Network, J. Univ. Sci. Technol. Beijing, 8(2001), No. 3, pp. 182-184.
Cite this article as:
Xinyu Liu, Bo Wen, Xinhua Wang, Qiang Niu, and Hong Chen, Prediction of Hot Ductility of Low-Carbon Steels Based on BP Network, J. Univ. Sci. Technol. Beijing, 8(2001), No. 3, pp. 182-184.
Xinyu Liu, Bo Wen, Xinhua Wang, Qiang Niu, and Hong Chen, Prediction of Hot Ductility of Low-Carbon Steels Based on BP Network, J. Univ. Sci. Technol. Beijing, 8(2001), No. 3, pp. 182-184.
Citation:
Xinyu Liu, Bo Wen, Xinhua Wang, Qiang Niu, and Hong Chen, Prediction of Hot Ductility of Low-Carbon Steels Based on BP Network, J. Univ. Sci. Technol. Beijing, 8(2001), No. 3, pp. 182-184.
Key Lab of New Packaging Materials & Technology of China National Packaging Corporation, Zhuzhou Engineering College, 412008, China
University of Science & Technology Beijing, Beijing 100083, China
中文摘要
The purpose of the research is to obtain an effective method to predict the hot ductility of low-carbon steels, which will be a reference to evaluate the crack sensitivity of steels. Several sub-networks modeled from BP network were constructed for different temperature use, and the measured reduction of area (AR) of 12 kinds of low-carbon steels under the temperature of 600 to 1000 ℃ were processed as training samples. The result of software simulation shows that the model established is relatively effective for predicting the hot ductility of steels.
The purpose of the research is to obtain an effective method to predict the hot ductility of low-carbon steels, which will be a reference to evaluate the crack sensitivity of steels. Several sub-networks modeled from BP network were constructed for different temperature use, and the measured reduction of area (AR) of 12 kinds of low-carbon steels under the temperature of 600 to 1000 ℃ were processed as training samples. The result of software simulation shows that the model established is relatively effective for predicting the hot ductility of steels.
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