《Table 1.The number of entries for powder materials in the dataset》

《Table 1.The number of entries for powder materials in the dataset》   提示：宽带有限、当前游客访问压缩模式
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《A novel approach to predict green density by high-velocity compaction based on the materials informatics method》


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An input training dataset is needed to build a quantitative statistical learning model.Each material we are interested in must be characterized via a representation in terms of one or more material descriptors.The dataset used in this work consists of chemical composition，the raw materials’properties，processing parameters，and the green density information from several metal powders used for HVC，including Fe，Cu，an Fe-based alloy，an Al alloy，a Ti alloy，and a composite.The dataset comprises a total of 223 entries，as shown in Table 1，which were all collected from previous experiments in our laboratory on an HYP 35-2 HVC machine with a maximum capacity of 2 kJ per stroke，a maximum velocity of 10 m/s，and a hammer mass of 42 kg.

图表编号XD0050012000    严禁用于非法目的
绘制时间2019.02.01
作者Kai-qi Zhang、Hai-qing Yin、Xue Jiang、Xiu-qin Liu、Fei He、Zheng-hua Deng、Dil Faraz Khan、Qing-jun Zheng、Xuan-hui Qu
绘制单位Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing、Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing、Beijing Key Laboratory of Materials Genome Engineering、Beij
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