Image driven machine learning methods for microstructure recognition

Abstract

Computer vision and machine learning methods were applied to the challenge of automatic microstructure recognition. Here, a case study on dendritic morphologies was performed. Two classification tasks were completed, and involved distinguishing between micrographs that depict dendritic morphologies from those that do not contain this particular microstructural feature (Task 1), and from those micrographs identified as depicting dendrites, different cross-sectional views (longitudinal or transverse) were identified (Task 2). Data sets were comprised of images taken over a range of magnifications, from materials with different compositions and varying orientations of microstructural features. Feature extraction and dimensionality reduction were performed prior to training machine learning algorithms to classify microstructural image data. Visual bag of words, texture and shape statistics, and pre-trained convolutional neural networks (deep learning algorithms) were used for feature extraction. Classification was then performed using support vector machine, voting, nearest neighbors, and random forest models. For each model, classification was completed using full (original size) and reduced feature vectors for each feature extraction method tested. Performance comparisons were done to evaluate all possible combinations of feature extraction, selection, and classifiers for the task of micrograph classification. Results demonstrate that pre-trained neural networks represent microstructure image data well, and when used for feature extraction yield the highest

Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures

“…Traditionally, microstructural images have been evaluated by human experts, both to interpret the micrographs themselves and to connect them to processing conditions and property outcomes. However, recent research in microstructure informatics has begun to explore applications of contemporary computer vision to construct microstructure representations suitable for use in machine learning and microstructure analytics tasks [1,2,3,4]. For example, [3] compare several image texture representations and find that off-the-shelf convolutional neural network (CNN) features can be applied to microstructure analytics tasks (e.g.…”

Predicting compressive strength of consolidated molecular solids using computer vision and deep learning

“…Future work. The above discussion suggests a number of avenues for further improving model performance for this task: (1) We expect synthesis of more material lots to automatically lead to gains in DL performance without any changes to the modelling approach. At what point DL performance eventually plateaus is a question for further investigation.…”

Deep Learning to Speed up the Development of Structure–Property Relations For Hexagonal Boron Nitride and Graphene

“…In recent years, transfer learning has become very popular in image recognition, especially with the advent of convolutional neural networks (CNNs) . However, this concept has hardly been used in material science, other than for classifying scanning electron microscope (SEM) images . Our results show that neural networks and transfer learning hold great potential in predicting P–S–P linkages.…”