Three Ti-6Al-4V alloy powders with median diameters of 103, 66 and 44 pm, respectively, were pressed by high-velocity compaction （HVC） technology and then sintered in vacuum. The effects of particle sizes on forming...Three Ti-6Al-4V alloy powders with median diameters of 103, 66 and 44 pm, respectively, were pressed by high-velocity compaction （HVC） technology and then sintered in vacuum. The effects of particle sizes on forming as well as properties of sintered samples were investigated. The results show that fine powders are more difficult to press than coarse powders and its compact density is lower too. But the sintered density of fine powders is obviously higher than that of coarse powders. Compared with the powders with 103 and 66 ~un in diameter, the green density with 44 ~rn diameter powders is lower, which is 85.1% of theoretical density （TD） at an impact energy of 913 J. After sintering at 1300 ~C for 2,5 h, the sintered density of the compacts with 44 pm diameter powders is the highest, and reaches 98.2% of TD. Moreover, the sintered sample with 44 pan in diameter has the highest hardness and compressive strength, which are HV 354 and 1265 MPa, respectively.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Project (51004040) supported by the National Natural Science Foundation of ChinaProject (20110952K) supported by Open Research Fund of State Key Laboratory of Powder Metallurgy of Central South University, China
文摘Three Ti-6Al-4V alloy powders with median diameters of 103, 66 and 44 pm, respectively, were pressed by high-velocity compaction （HVC） technology and then sintered in vacuum. The effects of particle sizes on forming as well as properties of sintered samples were investigated. The results show that fine powders are more difficult to press than coarse powders and its compact density is lower too. But the sintered density of fine powders is obviously higher than that of coarse powders. Compared with the powders with 103 and 66 ~un in diameter, the green density with 44 ~rn diameter powders is lower, which is 85.1% of theoretical density （TD） at an impact energy of 913 J. After sintering at 1300 ~C for 2,5 h, the sintered density of the compacts with 44 pm diameter powders is the highest, and reaches 98.2% of TD. Moreover, the sintered sample with 44 pan in diameter has the highest hardness and compressive strength, which are HV 354 and 1265 MPa, respectively.
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.
