Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data

Abstract

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.

arXiv comment18 pages, 13 figures, Integr Mater Manuf Innov (2021)

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Scikit-learn: Machine Learning in Python
• scikit-image: Image processing in Python
• Insightful classification of crystal structures using deep learning
• High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel

Cited by in corpus (5)

• Advances of Machine Learning in Materials Science: Ideas and Techniques
• A Comprehensive Survey on Machine Learning Driven Material Defect Detection
• Quantitative analysis of MoS${}_2$ thin film micrographs with machine learning
• Unveil the unseen: Exploit information hidden in noise
• A Review of Benchmarks for Visual Defect Detection in the Manufacturing Industry