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MIAMS: Weld defect segmentation in time-of-flight diffraction images based on multi-image attention and main structure analysis

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MEASUREMENT 2025年 246卷

作者： Yang, Deyan Jiang, Hongquan Yue, Kun Zhi, Zelin Jing, Deqiang Gao, Jianmin He, Ziyong Wang, Hui Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Shaanxi Special Equipment Inspection & Testing Ins Xian 710048 Peoples R China Dongfang Elect Machinery Co Ltd Deyang 618000 Peoples R China

Weld defect segmentation is crucial for realizing the defect rating of welded products and for evaluating the welding quality. However, the existing methods cannot accurately segment weld defect regions in time-of-flight diffraction (TOFD) images owing to blurring, weak texture variation, and surrounding noise and interference fringes. To address these issues, we propose a weld defect segmentation method based on multi-image attention and main structure analysis (MIAMS) for TOFD images, which includes three modules: multi-image feature fusion, two-branch feature extraction, and segmentation mask prediction. Additionally, we perform experimental validation using the weld TOFD images of large-scale spherical pressure tanks. The results indicate that MIAMS outperforms the existing semantic segmentation methods with a mean intersection over union and mean F-score of 86.18% and 92.28%, respectively. MIAMS is lightweight, and the results of defect segmentation closely match the results of expert evaluation in non-destructive testing, improving the quantitative accuracy of the defects.

关键词： time-of-flight diffraction Defect segmentation Multi-image attention Main structural analysis

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Performance evaluation test of the time-of-flight diffraction technique for welded joints of steel bridges

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JOURNAL OF TESTING AND EVALUATION 2008年第3期36卷 213-221页

作者： Miki, Chitoshi Nishikawa, Kazuhiro Shirahata, Hiromi Takahashi, Minoru Tokyo Inst Technol Dept Civil Engn Meguro Ku Tokyo 1528552 Japan Publ Works Res Inst Struct Res Grp Tsukuba Ibarakiken 3058516 Japan Publ Works Res Inst Planning & Res Adm Dept Tsukuba Ibarakiken 3058516 Japan Musashi Inst Technol Dept Civil Engn Setagaya Ku Tokyo 1588557 Japan

Ultrasonic testing can be applied to welded joints of steel bridges following a revision of the Japanese Specifications for Highway Bridges. The time-of-flight diffraction (TOFD) technique and the pulse echo technique are possible testing methods. However, the efficiency of the TOFD technique for steel bridges has until now not been investigated completely. A round robin test project using testing systems of several different companies was organized by the Ministry of Construction for this purpose. The performance of each participant system was evaluated and applicability of the TOFD technique was investigated.

关键词： nondestructive evaluation ultrasonic testing time-of-flight diffraction round robin test weld defect steel bridge

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A Numerical Model for Ultrasonic time-of-flight diffraction (TOFD) Testing of Austenitic Welds

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RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING 2023年第2期59卷 182-203页

作者： Hosseini, Seyyed H. Honarvar, Farhang KN Toosi Univ Technol Fac Mech Engn NDE Lab Tehran Iran

Austenitic stainless steel welded components are widely used in industrial applications. Due to the large grain size of these welds, ultrasonic testing of them is a difficult task. In this paper, the propagation of ultrasonic waves in austenitic stainless steel welds prepared by shielded metal arc welding (SMAW) and gas tungsten arc welding (GTAW) processes is modeled by finite element method. The grain structure of welds prepared by these two processes are completely different. First, the exact grain structure of each weld is extracted by examining the weld grain structures on a welded specimen. The weld areas of the two welds are then divided into several domains and the grain orientation of each domain is extracted. The elasticity tensor of the orthotropic weld material is also measured. In meshing of the finite element model, the grain orientation in each domain is accounted for by rotating the elasticity tensor of the elements of that domain along the direction of grains. The propagation of waves in time-of-flight diffraction (TOFD) ultrasonic testing of the welds is then simulated by using this model. Actual TOFD tests are also conducted on the welded test specimen in which crack-like slots are implanted. The TOFD signals collected from welds are then compared with the results obtained from the finite element model. Very good agreement is observed between the simulated and measured results indicating that the proposed finite element model can accurately model the SMAW and GTAW austenitic welds.

关键词： ultrasonic testing austenitic stainless steel welding finite element model anisotropy time-of-flight diffraction

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Detection method for weld defects in time-of-flight diffraction images based on multi-image fusion and feature hybrid enhancement

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 138卷

作者： Yang, Deyan Jiang, Hongquan Ai, Song Yang, Tianlun Zhi, Zelin Jing, Deqiang Gao, Jianmin Yue, Kun Cheng, Huyue Xu, Yongjun Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Dongfang Turbine Co Ltd State Key Lab Clean & Efficient Turbomachinery Pow Deyang 618000 Peoples R China Shaanxi Special Equipment Inspect & Testing Inst Xian 710048 Peoples R China

The accurate recognition of defects in the time-of-flight diffraction (TOFD) images of welds is important to improve the capability and efficiency of defect detection. The existing deep learning-based defect detection methods take a single image as input, without considering the fact that technicians need to observe the image "dynamically" during its evaluation, resulting in low accuracy and credibility of the defect detection results. To address these issues, combining deep learning techniques with TOFD inspection domain knowledge, this article proposes a multi-image fusion and feature hybrid enhancement-based weld defect detection method for TOFD images, comprising three parts: a single-to-multiple image decomposition module based on gain preprocessing, multi-image feature extraction module, and weld defect detection module based on feature hybrid enhancement. The developed method can realize a "dynamically changing" feature extraction and target detection of weld defects in TOFD images. The proposed method was experimentally verified using TOFD images of welds in largescale spherical pressure tanks. This method greatly surpassed the current state-of-the-art approaches, including You Only Look Once (YOLO) v9, YOLOv10, and Real-time DEtection TRansformer (RT-DETR), achieving a mean average precision of 82.0%, average precision for small-size targets of 45.2%, and average recall for small-size targets of 70.9%. The detection time for a single TOFD image with a resolution of 500 x 1350 pixels is 0.1287 s, satisfying the real-time requirements for weld TOFD inspection in practical engineering applications. The proposed method can also be extended to engineering applications such as intelligent detection of weld defects based on X-ray images.

关键词： time-of-flight diffraction Deep learning Defect detection Image decomposition Feature hybrid enhancement

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Method based on a multi-image feature fusion model for detecting weld defects in time-of-flight diffraction images

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INSIGHT 2024年第10期66卷 596-604页

作者： Yue, Kun Jiang, Hongquan Zhi, Zelin Yang, Deyan Cheng, Zhixiang Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Shaanxi Special Equipment Inspect & Testing Inst Xian 710065 Peoples R China

The time-of-flight diffraction ( TOFD) technology is widely used in weld defect inspection and the utilisation of TOFD images in automatic defect recognition is attracting interest from enterprises. However, the extant artificial intelligence (AI)-based object detection methods have limitations: they use only a single image, the ability to extract defect features is weak and they are easily affected by interference fringes. These limitations result in high miss and false detection rates. In this study, a weld defect object detection method based on a multi-image feature fusion model (MIFFM) is proposed. The original TOFD input image was preprocessed using adaptive Gaussian filtering (AGF) and anisotropic filtering (specifically, Perona-Malik (P-M) filtering) to remove noise and enhance the defect features in the image. Subsequently, the AGF preprocessed, P-M filtering preprocessed and original TOFD images were stacked and used as input images. To enhance the detection performance, the you only look once X (YOLOX) bidirectional feature fusion network combined with the non-monotonic dynamic Scylla-intersection over union (SIoU) loss function was constructed. Finally, the feasibility of the proposed method was verified using the TOFD image of a large-pressure spherical tank weld. The proposed method achieved a mean average precision (mAP) of 78.05%, which is higher than YOLOX by 4.59%.

关键词： time-of-flight diffraction weld defect detection feature fusion deep learning

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An end-to-end welding defect detection approach based on titanium alloy time-of-flight diffraction images

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第4期34卷 1895-1909页

作者： Zhi, Zelin Jiang, Hongquan Yang, Deyan Gao, Jianmin Wang, Quansheng Wang, Xiaoqiao Wang, Jingren Wu, Yongxiang Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn 28 West Xianning Rd Xian 710049 Peoples R China Shaanxi Special Equipment Inspect & Testing Inst Xian 710048 Peoples R China

The weld defect recognition of titanium alloy is of great significance for ensuring the safety and reliability of equipment. This study proposes a method based on the enlighten faster region-based convolutional neural network (EFRCNN) to recognize titanium alloy weld defects. First, by designing defect test blocks and using probes with different frequencies, a dataset of time-of-flight diffraction (TOFD) weld defect detections is constructed. Next, to overcome the problems of high data noise and low recognition accuracy, a parallel series multi-scale feature information fusion mechanism and a channel domain attention strategy are designed, and a deep learning network model based on the faster region-based convolution neural network (Faster R-CNN) is constructed. Finally, the proposed method is verified by the TOFD test data of titanium alloy welds. The results show that the proposed method can achieve a defect type recognition accuracy of more than 92%, especially in detecting cracks or a lack of fusion.

关键词： Titanium alloy time-of-flight diffraction Enlighten faster region-based convolutional neural network Defect recognition

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Enhancement of ultrasonic images for sizing of defects by time-of-flight diffraction

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NDT & E INTERNATIONAL 2010年第3期43卷 258-264页

作者： Sinclair, A. N. Fortin, J. Shakibi, B. Honarvar, F. Jastrzebski, M. Moles, M. D. C. Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada KN Toosi Univ Technol Fac Mech Engn Tehran Iran Olympus Corp Sales Dev & Tech Support Toronto ON Canada

A signal processing technique is presented for significantly sharpening the resolution of ultrasonic images, similar to those acquired in the nondestructive evaluation of girth welds in oil/gas pipelines. This enhancement allows a much improved estimate of the exact size of any detected anomaly in the weld, such that fracture mechanics can be used to gauge the probability of weld failure. The algorithm is based on the synthetic aperture focusing technique (SAFT), combined with a variation of Wiener filtering and autoregressive spectral extrapolation. An analytical model of the transducer is used to construct an appropriate reference spectrum for the deconvolution operation, and accounts for the dependence of a beam's frequency spectrum on the position of a flaw relative to the transmitter. Experimental results are used to provide an estimate of the improvement in flaw sizing accuracy. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Ultrasonic testing Weld inspection Signal processing time-of-flight diffraction Oil pipelines

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A normal weld recognition method for time-of-flight diffraction detection based on generative adversarial network

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年第1期35卷 217-233页

作者： Jiang, Hongquan Yang, Deyan Zhi, Zelin Jing, Qiangzheng Gao, Jianmin Tao, Chenyue Cheng, Zhixiang Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xian Special Equipment Inspect Inst Xian 710065 Peoples R China

time-of-flight diffraction (TOFD) has become a widely used nondestructive testing (NDT) technique, owing to its wide coverage, fast detection speeds, and high defect detection rates. However, compared with nondestructive radiographic testing images, TOFD image analysis requires more technicians and more difficult defect analysis. Owing to the improvements in weld manufacturing quality, there are fewer welds with defects;consequently, a large number of TOFD images have no defect information. The TOFD image analysis of normal welds occupies a lot of time in the weld evaluation process that easily leads to problems of missed and false detections and reduces the efficiency of overall weld evaluation. To solve these problems, a TOFD image reconstruction model based on the generative adversarial network (GAN) and a normal weld recognition method are proposed. First, combined with the TOFD image characteristics, an image-wave feature fusion (IWFF) module based on depth-separable convolution is designed, which integrates and analyzes the TOFD image and wave features, and an IWFF-GAN model is developed. Second, to improve the accuracy of normal weld recognition, a method for denoising the reconstructed error-feature map based on the total variation model is proposed. Finally, the proposed method is verified using the TOFD images of large-scale spherical pressure-tank welds. The results show that the method accurately distinguishes between the normal and abnormal welds, exhibiting a higher normal weld recognition accuracy. The area under the receiver operating characteristic curve is 0.9903.

关键词： time-of-flight diffraction Normal weld recognition Generative adversarial network Image reconstruction

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Enhancing ultrasonic time-of-flight diffraction measurement through an adaptive deconvolution method

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ULTRASONICS 2019年 96卷 175-180页

作者： Chen, Jian Wu, Eryong Wu, Haiteng Zhou, Hongming Yang, Keji Zhejiang Univ State Key Lab Fluid Power Transmiss & Control Hangzhou 310027 Zhejiang Peoples R China

Deconvolution is generally applied to improve the temporal resolution of ultrasonic signals. However, using this process in the time-of-flight diffraction (TOFD) measurement of small and shallow defects is challenging because TOFD signals are dispersive in space-frequency distribution. Particularly, determining the reference signal for deconvolution remains a critical barrier. To this end, an adaptive deconvolution method is proposed in this study. Using wavelet transform, we firstly decompose the TOFD signals into sub-band signals to standardise the space-frequency distribution. Then, sub-band signals with strong coherences are adaptively selected on the basis of coherence coefficient metric. Upon the opted sub-band signals, a lateral wave can be readily used as the reference signal, and TOFD signals can be reconstructed with established Wiener filtering and spectral extrapolation methods. The feasibility of the proposed method is validated with the TOFD measurement of a small side-drilled hole near the surface. Results show that the proposed method effectively separates overlapping TOFD signals and improves the axial resolution of a TOFD image.

关键词： time-of-flight diffraction Sub-band signals Wiener filtering and spectral extrapolation Adaptive deconvolution

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Quantitative texture measurements using neutron time-of-flight diffraction

Quantitative texture measurements using neutron time-of-flig...

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14th International Conference on Textures of Materials

作者： Vogel, SC Hartig, C Von Dreele, RB Wenk, HR Williams, DJ Los Alamos Natl Lab Los Alamos NM 87545 USA Tech Univ Hamburg D-21071 Hamburg Germany Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)087849975X

The neutron time-of-flight diffractometer HIPPO (High Pressure Preferred Orientation) was conceived and constructed as a joint effort between the University of California and Los Alamos National Laboratory. The unique detector coverage of HIPPO, with a total of 1360 He-3 detectors arranged in 50 panels, allows measurement of the sample orientation distribution function with rotation around only one axis. This capability allows design of sample environments, such as furnaces or loads cells, to measure texture in situ at non-ambient conditions. The high flux of the LANSCE spallation neutron source shortens count-times in some cases to less than 10 minutes to collect enough data for the determination of the ODF, opening the possibility of time-resolved texture studies. The capabilities for texture measurements of HIPPO are outlined and some examples of experiments conducted with HIPPO, such as phase transformation textures, are given.

关键词： neutron diffraction time-of-flight diffraction texture analysis Rietveld

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