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

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

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

2019

Scholarcy highlights

  • 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
  • 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
  • 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 confirmed by comparison with experimental results

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