Title

Spatio-Temporal 3-D Residual Networks for Simultaneous Detection and Depth Estimation of CFRP Subsurface Defects in Lock-In Thermography

Document Type

Article

Publication Date

4-1-2022

Abstract

Nondestructive thermography is a high-speed, low-cost, and safe solution for subsurface defects detection of carbon fiber reinforced polymer (CFRP) materials, providing essential quality control in aerospace, automobile, and sports industries. In this article, we build a reflective lock-in thermography system and construct a dataset that contains real-captured thermal image sequences of CFRP samples with various simulated internal defects under different excitation frequencies. Then, we present a novel 3-D convolutional neural network (CNN) model incorporating a combination of spatial and temporal convolutional filters and batch-size independent group normalization (GN) as a unified framework to process thermal image sequences captured by lock-in thermography for simultaneous subsurface defect detection and depth estimation. Finally, we define a multitask loss function to perform end-to-end training of both defect detection and depth estimation tasks based on the real-captured infrared sequences. Comparative experiments are carried out on CFRP specimens with artificial defects of various sizes/shapes and at different depths. Qualitative and quantitative results illustrate that our 3-D CNN model is capable of predicting accurate locations and depths of subsurface defects and performs favorably against the hand-crafted and CNN-based methods in lock-in thermography for individual defect detection and depth estimation tasks. The captured dataset and the source codes will be made publicly available.

Publication Source (Journal or Book title)

IEEE Transactions on Industrial Informatics

First Page

2571

Last Page

2581

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