Annotated digital image correlation displacement fields from fatigue crack growth experiments

TL;DR

This paper introduces a comprehensive, annotated dataset of digital image correlation displacement fields from fatigue crack growth experiments, supporting data-driven fracture mechanics research and machine learning applications.

Contribution

The paper provides a large, diverse, and well-annotated dataset of DIC displacement fields with standardized annotations and multiple resolutions, enabling advanced fracture mechanics analysis.

Findings

Dataset includes 8,794 displacement fields and 70,352 samples.

Annotations include crack tip locations and fracture descriptors.

Supports scalable use cases from education to high-capacity modeling.

Abstract

We present a curated dataset of planar displacement fields from eight fatigue crack growth experiments obtained via full-field digital image correlation (DIC). The dataset covers multiple aerospace-grade aluminium alloys, specimen geometries, material orientations, and load configurations, providing a diverse experimental basis for data-driven fracture mechanics research. Crack tip locations are consistently annotated using an iterative correction procedure applied to all measurements, and fracture mechanical descriptors like stress-intensity factors are provided as additional labels. The dataset comprises 8,794 unique experimentally observed displacement fields and a total of 70,352 supervised samples generated through standardized interpolation and augmentation. DIC data is provided as uniformly interpolated displacement grids at three standardized resolutions 28 x 28, 64 x 64, and 128 x 128 pixels, each available in three dataset sizes to support scalable use cases ranging from educational applications to high-capacity model development. Accompanying metadata and a Python interface facilitate filtering, loading, and integration into reproducible machine learning and fracture mechanics workflows.