# Correlation Between Meso-Defect and Fatigue Life Through Representing Feature Analysis for 6061-T6 Aluminum Alloys

## TL;DR

This study proposes a method to predict the fatigue life of 6061-T6 aluminum alloys by analyzing mesoscopic defects using X-ray CT and simulation.

## Contribution

A novel fatigue life prediction method incorporating weighted meso-defect-representing features is introduced.

## Key findings

- Porosity, shape, and location were identified as key meso-defect-representing features.
- Weights for these features were determined using FEM simulation and correlation analysis.
- The proposed method effectively correlates meso-defect features with fatigue life.

## Abstract

Fatigue strength is vital for engineering applications of aluminum alloys. Accurate models incorporating mesoscopic defect-representing features are one of the issues for accurate fatigue strength prediction. A fatigue life prediction method based on meso-defect-representing features is proposed in this study. Based on staged fatigue damage, meso-defect data was obtained by X-ray CT. After 3D reconstruction and simplification, porosity, shape, and location were selected as the meso-defect-representing features using correlation coefficient analysis. Weights of meso-defect features were determined through FEM simulation. A mesoscopic damage variable incorporating the weights of porosity, shape, and location for meso-defect was defined. Correlation between fatigue life and meso-defect features was established through the mesoscopic damage variable. Experimental verification results showed that the prediction method is an effective method for fatigue life assessment.