Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships

TL;DR

This study develops and evaluates machine learning methods, including generative adversarial networks, for microstructure image analysis to predict processing conditions in limited data scenarios, achieving high classification accuracy.

Contribution

It introduces a novel microstructure representation and demonstrates the use of GANs to augment small datasets for improved microstructure classification.

Findings

Achieved 95.1% F1 score in classifying processing conditions.

Traditional phase fraction methods are insufficient for small, imbalanced datasets.

GANs can generate useful artificial microstructure images for data augmentation.

Abstract

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy (uranium-molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on the F1 score. A F1 score of 95.1% was achieved for distinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions. We find that our newly developed microstructure representation describes image data well, and the traditional approach of utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relatively small, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.