Grain and Grain Boundary Segmentation using Machine Learning with Real and Generated Datasets
We report significantly improved accuracy of grain boundary segmentation
using Convolutional Neural Networks (CNN) trained on a combination of real and
generated data. Manual segmentation is accurate but time-consuming, and
existing computational methods are faster but often inaccurate. To combat this
dilemma, machine learning models can be used to achieve the accuracy of manual
segmentation and have the efficiency of a computational method. An extensive
dataset of from 316L stainless steel samples is additively manufactured,
prepared, polished, etched, and then microstructure grain images were
systematically collected. Grain segmentation via existing computational methods
and manual (by-hand) were conducted, to create "real" training data. A Voronoi
tessellation pattern combined with random synthetic noise and simulated
defects, is developed to create a novel artificial grain image fabrication
method. This provided training data supplementation for data-intensive machine
learning methods. The accuracy of the grain measurements from microstructure
images segmented via computational methods and machine learning methods
proposed in this work are calculated and compared to provide much benchmarks in
grain segmentation. Over 400 images of the microstructure of stainless steel
samples were manually segmented for machine learning training applications.
This data and the artificial data is available on Kaggle.