Water atomized pure iron powder was compacted by high velocity compaction （HVC） with and without upper relaxation assist （URA） device. The influence of URA device on green density, spring back, green strength and ...Water atomized pure iron powder was compacted by high velocity compaction （HVC） with and without upper relaxation assist （URA） device. The influence of URA device on green density, spring back, green strength and hardness was studied. Morphological characteristics of the samples were observed by scanning electron microscope （SEM）. Green strength of the samples was measured by computer controlled universal testing machine. The results show that as stroke length increases, the green density, green strength and hardness of the compacts increase gradually. At the identical stroke length, the green density of the compacts pressed with URA devise was 2% higher than the compacts pressed without URA device. The green strength and hardness of the compacts pressed with URA device were higher than the compacts pressed without URA device. Furthermore, the radial spring back of the compacts decreased gradually with the increment in stroke length, whilst that of compacts prepared with URA device was lower.展开更多
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 great...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/cm^3 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.展开更多
基金financially supported by the National Natural Science Foundation of China(NSFC)(No.51172018)the National High Technical Research and Development Programme of China(No.2009BAE74B00)+1 种基金the National Basic Research Program of China(No.2006CB605207)MOE Program for Changjiang Scholars and Innovative Research Team in University of China(No.I2P407)
文摘Water atomized pure iron powder was compacted by high velocity compaction （HVC） with and without upper relaxation assist （URA） device. The influence of URA device on green density, spring back, green strength and hardness was studied. Morphological characteristics of the samples were observed by scanning electron microscope （SEM）. Green strength of the samples was measured by computer controlled universal testing machine. The results show that as stroke length increases, the green density, green strength and hardness of the compacts increase gradually. At the identical stroke length, the green density of the compacts pressed with URA devise was 2% higher than the compacts pressed without URA device. The green strength and hardness of the compacts pressed with URA device were higher than the compacts pressed without URA device. Furthermore, the radial spring back of the compacts decreased gradually with the increment in stroke length, whilst that of compacts prepared with URA device was lower.
基金financially supported by the National Key Research and Development Program of China (No. 2016YFB0700503)the National High Technology Research and Development Program of China (No. 2015AA034201)+2 种基金the Beijing Science and Technology Plan (No. D161100002416001)the National Natural Science Foundation of China (No. 51172018)Kennametal Inc
文摘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/cm^3 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.
