# Toward quantitative fractography using convolutional neural networks

arXiv: https://arxiv.org/abs/1908.02242 · HTML: https://mlsanity.com/paper/1908.02242/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression](https://mlsanity.com/paper/2103.08349/) (2021)
- [Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images](https://mlsanity.com/paper/2203.13875/) (2022)
- [Low-Magnification SEM May Suffice: Interpretable Deep Learning for Multi-Scale Fracture-Cause Classification in Zirconia-Toughened Alumina](https://mlsanity.com/paper/2605.29798/) (2026)

## Abstract (arXiv)

The science of fractography revolves around the correlation between topographic characteristics of the fracture surface and the mechanisms and external conditions leading to their creation. While being a topic of investigation for centuries, it has remained mostly qualitative to date. A quantitative analysis of fracture surfaces is of prime interest for both the scientific community and the industrial sector, bearing the potential for improved understanding on the mechanisms controlling the fracture process and at the same time assessing the reliability of computational models currently being used for material design. With new advances in the field of image analysis, and specifically with machine learning tools becoming more accessible and reliable, it is now feasible to automate the process of extracting meaningful information from fracture surface images. Here, we propose a method of identifying and quantifying the relative appearance of intergranular and transgranular fracture events from scanning electron microscope images. The newly proposed method is based on a convolutional neural network algorithm for semantic segmentation. The proposed method is extensively tested and evaluated against two ceramic material systems (Al2O3,MgAl2O4) and shows high prediction accuracy, despite being trained on only one material system (MgAl2O4). While here attention is focused on brittle fracture characteristics, the method can be easily extended to account for other fracture morphologies, such as dimples, fatigue striations, etc.