Work

U-Net driven semantic segmentation for detection and quantification of cracks on gas turbine blade tips

Year: 2025

Type: article

Abstract: Crack detection and quantification on gas turbine blades is crucial for component validation. Gas turbine blades operate under extreme thermal and mechanical stresses, making them particularly susceptible to crack formation. At the same time deterministic predictions of crack formation are subject to high uncertainty in material data and actual loading conditions. Accurate detection and quantification of cracks, therefore, is essential for the validation and calibration of life predictions in order to prevent in-service failures, to extend component lifespan, and to reduce maintenance costs. This study introduces a U-Net based semantic segmentation model designed to automate crack detection on turbine blade tips. The model was trained on a dataset of 210 images with and without evidence of cracks, each divided into 128 × 128 pixel patches. Data augmentation techniques were applied to address the class imbalance between cracked and non-cracked pixels. The U-Net architecture, optimized with a Dice loss function, achieved a validation IoU of 0.7557, along with approximately 85\% recall and precision in identifying cracked pixels. The pixel-based accuracy of the model primarily affects the quantification of cracks rather than their identification. A sliding window pipeline was implemented to extend the model’s applicability, enabling segmentation of entire blade tip images for comprehensive crack localization. While the model may occasionally miss low-contrast cracks, it holds potential as a supplementary tool for manual inspection as part of the life prediction validation. By providing automated crack localization and quantification, the model can assist in analyzing crack characteristics relative to engine operating conditions.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2025.108864

Authors: Dongsuk Kim, Ulf Gerstberger, Majid Asli, Klaus Höschler

Institutions: Brandenburg University of Technology Cottbus-Senftenberg, Rolls-Royce (Germany)

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,940