Automatically Predict Material Properties with Microscopic Image Example Polymer Compatibility

TL;DR

This paper presents a machine learning approach using CNNs and transfer learning to automatically predict polymer miscibility from SEM images, achieving high accuracy and providing a quantitative criterion.

Contribution

The study introduces an automated method for polymer miscibility prediction from microscopic images, combining CNNs and transfer learning for improved accuracy and quantification.

Findings

Achieved up to 94% accuracy in miscibility recognition

Developed a quantitative criterion for polymer miscibility

Demonstrated wide applicability to material microstructure analysis

Abstract

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.