A Machine Learning based Automatic defect detection in non stationary thermal wave imaging
Year: 2025
Type: article
Abstract: The detection of subsurface anomalies is crucial for assessing the structural integrity of materials in various industrial applications. Non-stationary thermal wave imaging has emerged as a reliable non-destructive testing (NDT) technique for identifying defects in composite materials such as glass fiber reinforced polymer (GFRP) and carbon fiber reinforced polymer (CFRP). This research proposes a machine learning-based framework for automatic defect detection using thermal wave imaging data. Supervised learning algorithms, including Decision Tree (DT), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN), are implemented to classify subsurface defects based on their size and depth. The system is evaluated using signal to-noise ratio (SNR) and detection accuracy metrics to ensure robustness and reliability. The proposed approach enhances defect identification efficiency by leveraging advanced data processing techniques, making it a scalable solution for industrial applications. Experimental results demonstrate improved detection performance compared to conventional defect detection methods
Authors: Dr. A Vijayalakshmi, Y Harshita, B Tejomayi, N S N M Subhramanaya, U Jaswanth Reddy
Institution: Amrita Vishwa Vidyapeetham
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893234452