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Volume 26 Issue 2
Feb.  2019
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文章导航 >  矿物冶金与材料学报（英文）  > 2019  >  26(2): 194-201
Kai-qi Zhang, Hai-qing Yin, Xue Jiang, Xiu-qin Liu, Fei He, Zheng-hua Deng, Dil Faraz Khan, Qing-jun Zheng,  and Xuan-hui Qu, A novel approach to predict green density by high-velocity compaction based on the materials informatics method, Int. J. Miner. Metall. Mater., 26(2019), No. 2, pp. 194-201. https://doi.org/10.1007/s12613-019-1724-x
Cite this article as:
Kai-qi Zhang, Hai-qing Yin, Xue Jiang, Xiu-qin Liu, Fei He, Zheng-hua Deng, Dil Faraz Khan, Qing-jun Zheng,  and Xuan-hui Qu, A novel approach to predict green density by high-velocity compaction based on the materials informatics method, Int. J. Miner. Metall. Mater., 26(2019), No. 2, pp. 194-201. https://doi.org/10.1007/s12613-019-1724-x
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研究论文

A novel approach to predict green density by high-velocity compaction based on the materials informatics method

  • Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China

  • Chongqing Engineering Technology Research Center for Light Alloy and Processing, Chongqing 404100, China

  • Kennametal Inc, 1600 Technology Way, PA 15650, USA

  • School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China

  • Department of Physics, University of Science and Technology Bannu, Bannu 28100, Pakistan

  • Beijing Key Laboratory of Materials Genome Engineering, Beijing 100083, China

  • Beijing Laboratory of Metallic Materials and Processing for Modern Transportation, Beijing 100083, China

  • 通讯作者:

    Hai-qing Yin    E-mail: hqyin@ustb.edu.cn

  • High-velocity compaction is an advanced compaction technique to obtain high-density compacts at a compaction velocity of ≤ 10 m/s. It was applied to various metallic powders and was verified to achieve a density greater than 7.5 g/cm3 for the Fe-based powders. The ability to rapidly and accurately predict the green density of compacts is important, especially as an alternative to costly and time-consuming materials design by trial and error. In this paper, we propose a machine-learning approach based on materials informatics to predict the green density of compacts using relevant material descriptors, including chemical composition, powder properties, and compaction energy. We investigated four models using an experimental dataset for appropriate model selection and found the multilayer perceptron model worked well, providing distinguished prediction performance, with a high correlation coefficient and low error values. Applying this model, we predicted the green density of nine materials on the basis of specific processing parameters. The predicted green density agreed very well with the experimental results for each material, with an inaccuracy less than 2%. The prediction accuracy of the developed method was thus confirmed by comparison with experimental results.
    • Research Article

    A novel approach to predict green density by high-velocity compaction based on the materials informatics method

    + Author Affiliations
      Abstract
    • High-velocity compaction is an advanced compaction technique to obtain high-density compacts at a compaction velocity of ≤ 10 m/s. It was applied to various metallic powders and was verified to achieve a density greater than 7.5 g/cm3 for the Fe-based powders. The ability to rapidly and accurately predict the green density of compacts is important, especially as an alternative to costly and time-consuming materials design by trial and error. In this paper, we propose a machine-learning approach based on materials informatics to predict the green density of compacts using relevant material descriptors, including chemical composition, powder properties, and compaction energy. We investigated four models using an experimental dataset for appropriate model selection and found the multilayer perceptron model worked well, providing distinguished prediction performance, with a high correlation coefficient and low error values. Applying this model, we predicted the green density of nine materials on the basis of specific processing parameters. The predicted green density agreed very well with the experimental results for each material, with an inaccuracy less than 2%. The prediction accuracy of the developed method was thus confirmed by comparison with experimental results.
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