Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

TL;DR

This paper demonstrates the use of instance segmentation, specifically Mask R-CNN with transfer learning, for analyzing metal powder images to measure particle size, satellite content, and microstructural features, enabling automated microstructural characterization.

Contribution

It introduces a novel application of instance segmentation for direct measurement of satellite content and microstructural features in metal powders, with minimal training data.

Findings

Particle size measurements agree with laser scattering data

Satellite content measurements are consistent with expected trends

Method demonstrates flexibility for microstructural analysis in materials science

Abstract

We propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we provide a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel Database, demonstrating the flexibility of the technique.