A novel approach to predict green density by high-velocity compaction based on the materials informatics method
来源期刊：International Journal of Minerals Metallurgy and Materials2019年第2期
论文作者：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
文章页码：194 - 201
摘 要：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.
Kai-qi Zhang1，Hai-qing Yin1,2,3，Xue Jiang1，Xiu-qin Liu4，Fei He1，Zheng-hua Deng1,5，Dil Faraz Khan6，Qing-jun Zheng7，Xuan-hui Qu1
1. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing2. Beijing Key Laboratory of Materials Genome Engineering3. Beijing Laboratory of Metallic Materials and Processing for Modern Transportation4. School of Mathematics and Physics, University of Science and Technology Beijing5. Chongqing Engineering Technology Research Center for Light Alloy and Processing6. Department of Physics, University of Science and Technology Bannu7. Kennametal Inc
摘 要：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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