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

arXiv: https://arxiv.org/abs/2007.13417 · HTML: https://mlsanity.com/paper/2007.13417/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset](https://mlsanity.com/paper/2203.04183/) (2022)
- [Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis](https://mlsanity.com/paper/2405.00876/) (2024)
- [Digital Fingerprinting of Microstructures](https://mlsanity.com/paper/2203.13718/) (2022)
- [Automated analysis of continuum fields from atomistic simulations using statistical machine learning](https://mlsanity.com/paper/2206.08048/) (2022)
- [Context Determines Optimal Architecture in Materials Segmentation](https://mlsanity.com/paper/2602.04154/) (2026)
- [Ensemble of Pre-Trained Neural Networks for Segmentation and Quality Detection of Transmission Electron Microscopy Images](https://mlsanity.com/paper/2209.01908/) (2022)

## Abstract (arXiv)

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.