Document Type
Article
Source Publication Title
Journal of Composite Materials
DOI
https://doi.org/10.1177/00219983241292779
Abstract
Composite laminates, known for their strength and flexibility, are widely used but can delaminate under certain barely visible impact. Ultrasound Transmission (UT) scans can detect such failures, but the noise in the scans makes automation of delamination morphology challenging. Machine learning could solve this, but it requires substantial training data. Furthermore, given the considerable time and cost of conducting impact tests on composite laminates, such data remains sparse. This study overcomes data sparsity by enlarging UT scan dataset using image augmentation techniques and synthetic data generation methods. A combination of rotation and elastic deformation of real UT images produced augmented data. Synthetic data was generated by mimicking the statistical variations in real data, including delamination length and rotation per ply, and adding gradients to match the original image noise. A U-Net based machine learning segmentation approach was utilized to capture local and global information through a series of convolutions to apply per-pixel classification. Systematic experimentation was conducted to study the influence of adding augmented and synthetic data on segmentation accuracy. The results show that the use of augmented data increases accuracy significantly. With the addition of synthetic data to the augmented data, there is a marginal improvement in accuracy. However, feature edge accuracy is significantly improved. The preliminary results presented here show the promising use of machine learning techniques for complex, non-destructive inspection methods.
Publication Date
11-9-2024
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Davidson, Paul; Nguyen, Don; DeMille, Karen; and Ranatunga, Vipul, "Machine learning based segmentation of delamination patterns from sparse ultrasound data of barely visible impact damage in composites" (2024). SAGE Open Access Agreement Publications-Archive. 75.
https://mavmatrix.uta.edu/utalibraries_sageoapubs/75