Many material properties are manifested in the morphological appearance and characterized with microscopic image, such as scanning electron microscopy (SEM). Polymer miscibility is a key physical quantity of polymer material and commonly and intuitively judged by SEM images. However, human observation and judgement for the images is time-consuming, labor-intensive and hard to be quantified. Computer image recognition with machine learning method can make up the defects of artificial judging, giving accurate and quantitative judgement. We achieve automatic miscibility recognition utilizing convolution neural network and transfer learning method, and the model obtains up to 94% accuracy. We also put forward a quantitative criterion for polymer miscibility with this model. The proposed method can be widely applied to the quantitative characterization of the microstructure and properties of various materials.

翻译：许多材料性能体现在其微观形貌上，并可通过扫描电子显微镜（SEM）等显微图像进行表征。聚合物相容性是高分子材料的关键物理量，通常通过SEM图像进行直观判断。然而，人工观察和判断图像耗时费力且难以量化。基于机器学习方法的计算机图像识别技术可弥补人工判断的不足，提供精确且定量的评估结果。我们利用卷积神经网络与迁移学习方法实现了聚合物相容性的自动识别，该模型准确率高达94%。此外，我们基于该模型提出了聚合物相容性的定量判据。所提出的方法可广泛应用于各类材料微观结构与性能的定量表征。