Abstract Reducing porosity in sintered products allows enhancement of several of its properties such as conductivity (electrical and thermal), strength, and translucency. One of the key challenges to porosity reduction and property improvement during sintering is abnormal grain growth (AGG). Abnormal grain growth occurs when a certain energetically favored particle becomes significantly larger than other particles in the matrix. This leads to an increase in porosity due to uneven particle size. There is no simple a priori test to determine whether a given powder sample will exhibit abnormal grain growth, when subjected to sintering. Here we show that a machine learning based approach predicts abnormal grain growth in powdered samples prior to actual sintering. This approach has the potential to allow for pre-selection of appropriate powder samples with an accuracy of 82%, to minimize the risk of abnormal grain growth in practical sintering processes.

中文翻译：

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基于机器学习对烧结驱动异常晶粒生长的粉末样品的先验预测

摘要 减少烧结产品的孔隙率可以增强其多种性能，例如导电性（电和热）、强度和半透明性。在烧结过程中减少孔隙率和改善性能的主要挑战之一是异常晶粒生长 (AGG)。当特定的能量优势粒子变得明显大于基体中的其他粒子时，就会发生异常的晶粒生长。由于粒度不均匀，这会导致孔隙率增加。没有简单的先验测试来确定给定的粉末样品在进行烧结时是否会表现出异常的晶粒生长。在这里，我们展示了基于机器学习的方法在实际烧结之前预测粉末样品中的异常晶粒生长。