SIFBench: An Extensive Benchmark for Fatigue Analysis

TL;DR

SIFBench is a comprehensive, open-source benchmark database with over 5 million finite element simulation-derived crack geometries, designed to advance machine learning-based fatigue crack growth prediction.

Contribution

This paper introduces SIFBench, the first large-scale, well-organized dataset for ML-based stress intensity factor prediction in fatigue analysis.

Findings

Baseline ML models achieve promising prediction accuracy.

The dataset enables standardized evaluation of different ML approaches.

SIFBench facilitates research in damage tolerance and predictive maintenance.

Abstract

Fatigue-induced crack growth is a leading cause of structural failure across critical industries such as aerospace, civil engineering, automotive, and energy. Accurate prediction of stress intensity factors (SIFs) -- the key parameters governing crack propagation in linear elastic fracture mechanics -- is essential for assessing fatigue life and ensuring structural integrity. While machine learning (ML) has shown great promise in SIF prediction, its advancement has been severely limited by the lack of rich, transparent, well-organized, and high-quality datasets. To address this gap, we introduce SIFBench, an open-source, large-scale benchmark database designed to support ML-based SIF prediction. SIFBench contains over 5 million different crack and component geometries derived from high-fidelity finite element simulations across 37 distinct scenarios, and provides a unified Python interface for seamless data access and customization. We report baseline results using a range of popular ML models -- including random forests, support vector machines, feedforward neural networks, and Fourier neural operators -- alongside comprehensive evaluation metrics and template code for model training, validation, and assessment. By offering a standardized and scalable resource, SIFBench substantially lowers the entry barrier and fosters the development and application of ML methods in damage tolerance design and predictive maintenance.