Generative Independent Component Thermography for Improved Defect Detection of Carbon Fiber Composites
- Resource Type
- Conference
- Authors
- Liu, Kaixin; Chen, Meili; Wang, Zhiwen; Yao, Yuan; Yang, Jianguo; Liu, Yi
- Source
- 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Data Driven Control and Learning Systems Conference (DDCLS), 2020 IEEE 9th. :845-849 Nov, 2020
- Subject
- Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Gallium nitride
Data models
Optical fiber networks
Data analysis
Generative adversarial networks
Convolution
Polymers
non-destructive evaluation
deep learning
generative adversarial network
independent component thermography
carbon fiber reinforced polymer
- Language
As one of the popular techniques for non-destructive evaluation, infrared thermography often requires the assistance of data analysis models to help defect detection and identification. A novel generative independent component thermography (GICT) framework for defect detection in polymer composites is proposed. It utilizes a deep convolutional generative adversarial network to generate more informative images, which enhances the diversity of thermography data. The defect detection performance of sequential ICT-based thermographic data analysis can be enhanced. The feasibility of GICT is illustrated with its application to the defect detection of a carbon fiber reinforced polymer specimen.