  Unsupervised machine learning offers significant opportunities for extracting
knowledge from unlabeled data sets and for achieving maximum machine learning
performance. This paper demonstrates how to construct, use, and evaluate a high
performance unsupervised machine learning system for classifying images in a
popular microstructural dataset. The Northeastern University Steel Surface
Defects Database includes micrographs of six different defects observed on
hot-rolled steel in a format that is convenient for training and evaluating
models for image classification. We use the VGG16 convolutional neural network
pre-trained on the ImageNet dataset of natural images to extract feature
representations for each micrograph. After applying principal component
analysis to extract signal from the feature descriptors, we use k-means
clustering to classify the images without needing labeled training data. The
approach achieves $99.4\% \pm 0.16\%$ accuracy, and the resulting model can be
used to classify new images without retraining This approach demonstrates an
improvement in both performance and utility compared to a previous study. A
sensitivity analysis is conducted to better understand the influence of each
step on the classification performance. The results provide insight toward
applying unsupervised machine learning techniques to problems of interest in
materials science.
