Automated Detection of Delamination Defects in Composite Laminates from Ultrasonic Images Based on Object Detection Networks

Shearography, an effective non-destructive testing tool, is widely employed for detecting defects in composite materials. It detects internal defects by detecting deformation anomalies, offering advantages such as full-field, non-contact measurement, and high accuracy. Defect size is a critical parameter determining structure performance stability and service life. However, manual inspection is the primary method for defect size measurement in this technique, leading to inefficiency and low accuracy. To address this issue, this study established a defect recognition and high-precision automatic measurement method based on the U-Net deep learning network. First, a high-precision one-time calibration method for all system parameters was developed. Second, U-Net was employed to segment the measured image, identifying defect location and subimage. Finally, defect size was accurately calculated by combining calibration parameters and segmented defect subimage. The proposed method yielded a measurement error of less than 5% and a real-time dynamic detection rate of 14 fps, demonstrating potential for automated quantitative defect detection.

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Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network

Yue,

Sha,

Sun

et al. 2024

Applied Sciences

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After rolling, TP2 copper tubes exhibit defects such as sawtooth marks, cracks, and uneven wall thickness after joint drawing, which severely affects the quality of the finished copper tubes. To study the effect of drawing process parameters on wall thickness uniformity, an ultrasonic detection platform for measuring the wall thickness of rolled copper tubes was constructed to verify the accuracy of the experimental equipment. Using the detected data, a finite element model of drawn copper tubes was established, and numerical simulation studies were conducted to analyze the influence of parameters such as outer die taper angle, drawing speed, and friction coefficient on drawing force, maximum temperature, average wall thickness, and wall thickness uniformity. To address the problem of the large number of finite element model meshes and low solution efficiency, the wall thickness uniformity was predicted using a radial basis function (RBF) neural network, and parameter optimization was performed using the particle swarm optimization (PSO) algorithm. The research results show that the RBF neural network can accurately predict wall thickness uniformity, and using the PSO optimization algorithm, the best parameter combination can reduce the wall thickness uniformity after drawing in finite element simulation.

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Automated Detection of Delamination Defects in Composite Laminates from Ultrasonic Images Based on Object Detection Networks

Cited by 3 publications

References 26 publications

Automatic and Accurate Determination of Defect Size in Shearography Using U-Net Deep Learning Network

Automatic and Accurate Determination of Defect Size in Shearography Using U-Net Deep Learning Network

Automatic and accurate determination of defect size in shearography using U-Net deep learning network

Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network

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