Fully Convolutional Neural Network With GRU for 3D Braided Composite Material Flaw Detection

Abstract

Automated ultrasonic signal classification systems are often utilized for the recognition of a large number of ultrasonic signals in engineering materials. Existing defect classification methods are mainly image-based and serve to extract features for various defects. In this paper, we propose a novel detection baseline model based on a fully convolution network (FCN) and gated recurrent unit (GRU) to classify ultrasonic signals from flawed 3D braided composite specimens with debonding defects. In the proposed algorithm, the proposed Gated Recurrent Unit Fully Convolutional Network (GRU-FCN) is used to extract temporal characteristics of ultrasonic signals; the GRU module is key to enhancing the performance of FCNs. Experimental results on an in-house dataset indicated that the proposed model performs very well against all baselines. We also developed a scheme to interpret the relationship between A-scan and C-scan images and a 3D depth model representation to visualize the location information of defects.

Keywords

• Ultrasonic signal classification
• fully convolution networks
• gated recurrent unit
• 3D braided composite specimens
• time series
• C-scan images