Nondestructive Testing, Multi-Field Information Fusion, and AI-Aided Signal Processing

Editors

Jianbo Wu

Sichuan University

Bo Feng

Huazhong University of Science and Technology

Maciej Roskosz

AGH University of Science and Technology

Impact

Original Research

12 January 2023

Tire appearance defect detection method via combining HOG and LBP features

Hongbin Liu

, 3 more and

Chengdong Li

Tire appearance defect detection based on machine vision is an effective technology to improve the tire production quality. The detection process can be completed by the way of non-destructive testing. Therefore, more and more researchers are paying attention to this technology. However, tires are characterized by single block colors and various defects. It is a great challenge to accurately detect tire appearance defects. To complete the task of detecting tire defects, this paper presents a novel tire appearance defect detection method via combining histogram of oriented gradients (HOG) and local binary pattern (LBP) features. First, we construct a tire image dataset to provide defective and normal tire images. Then, histogram of oriented gradients and local binary pattern features of tire images are, respectively, extracted and used to train the support vector machine (SVM) classifier. Finally, the support vector machine classifier calculates the prediction scores of the test images via combining the histogram of oriented gradients and local binary pattern features. These scores can be utilized to determine whether the test image is a defective or a normal tire image, and the goal of tire appearance defect detection is achieved. Conducted on the tire image dataset, our method has verified the effectiveness of detecting tire detects, and the mean accuracy is improved more than 1.6% than the algorithm that only uses the histogram of oriented gradients or local binary pattern feature. The experimental results demonstrate that the combination of HOG and LBP features can increase tire appearance defect detection accuracy.

3,279 views

15 citations

Original Research

13 December 2022

Crack identification of automobile steering knuckle fluorescent penetrant inspection based on deep convolutional generative adversarial networks data enhancement

Yun Yang

, 3 more and

Long Li

As a key safety component of automobiles, automobile steering knuckles must be subjected to strict quality control. Currently, the identification of cracks in finished products primarily relies on manual identification of fluorescent penetrant detection. Owing to the complex shape of the workpiece, the interference of the displayed image and the small sample size, the accuracy of the automatic discrimination result of the fluorescent penetrant detection image is directly reduced. Therefore, this study proposed a data augmentation method based on deep convolutional generative adversarial networks (DCGAN) for crack identification in automotive steering knuckle fluorescent penetration inspection images. An image acquisition platform was built for fluorescence penetration detection of automobile steering knuckles, and fluorescence display images of various parts of the workpiece were collected. Based on the feature analysis of the displayed image, the image was preprocessed to suppress relevant interference and extract crack candidate regions. Further, using the original crack image to train DCGAN, several crack image samples were generated, the ResNet network was trained with the expanded dataset, and the extracted candidate regions were identified. Finally, the experimental results show that the recall rate of the crack recognition method used in this paper is 95.1%, and the accuracy rate is 90.8%, which can better identify the crack defects in the fluorescent penetrant inspection image, compared with the non-generative data enhancement method.

2,125 views

10 citations

(A) Section diagram of the outer bearing ring (B) Section diagram of the inner bearing ring.

Original Research

10 January 2023

Influence of curvature radius on the axial crack signal of the magnetic flux leakage detection for tapered roller bearing rings

Yun Yang

, 3 more and

Zhenyu Liang

1,436 views

1 citations

Diffuse ultrasonic wave pattern (D)–(F) and localization result (G)–(I) in different additive manufactured short fiber reinforced composites (A)–(C).

Original Research

17 November 2022

Numerical study of defect localization in additive manufactured short fiber reinforced composites with diffuse ultrasonic wave inspection

Yue Peng

, 3 more and

Qi Zhu

1,220 views

2 citations

Flow chart for damage location and imaging identification of composite Manuscript Formatting.

Original Research

11 November 2022

Locating and imaging composite damage based on frequency spectrum detection of lamb waves

Wenchao Zhang

, 2 more and

Hongjuan Yang

2,396 views

4 citations

Original Research

05 September 2022

Research on damage visualization of concrete structures based on electrical resistance tomography

Chenhui Su

, 3 more and

Chengdong Li

1,908 views

0 citations

Original Research

29 August 2022

Effects of curvature radius on flexible eddy current array sensors for the curved surface of metal components

Zhiyang Deng

, 5 more and

Yihua Kang

The flexible eddy current array sensor owns the advantages of high sensitivity and strong adaptability, but the results are always affected by the curvature radius of complex curved surfaces. The relationship between the curvature radius of the curved surface and detection signals for surface-breaking cracks is mainly discussed. The change of magnetic field caused by the curved surface in the present eddy current testing is specially pointed out, which manifest themselves in the compression or enhancement of the testing signal in its peak value and the baseline drifts. Simulation and experimental results indicate that the concave surface weakens the signal, while the convex surface enhances the signal. The signal amplitude decreases with the decrease in the curvature radius for the concave surface, while it is the opposite for the convex surface. Meanwhile, coil spacing significantly affects the amplitude–curvature radius curve. Furthermore, the fluctuation characteristic affected by the curvature radius under different coil spacing is analyzed. The discovery and results will benefit the quantitative evaluation of flexible eddy current array testing.

2,868 views

6 citations