Acoustic Non-Destructive Testing Technology in Concrete Bridge Inspection and Pile Foundation Detection

This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview of a certain bridge construction project studied and acoustic non-destructive testing technology and the application of acoustic non-destructive testing technology in actual testing.This analysis hopes to provide some guidelines for acoustic non-destructive testing of modern concrete bridge projects.

I-DCGAN and TOPSIS-IFP:A simulation generation model for radiographic flaw detection images in light alloy castings and an algorithm for quality evaluation of generated images

The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.

Optical techniques in non-destructive detection of wheat quality:A review

Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low efficiency.With the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality detection.These methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent years.In this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail.Firstly,the principle and process of common optical non-destructive detection methods for wheat quality are introduced.Then,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further discussed.It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and efficiency.Finally,the research of optical technology in wheat quality detection is prospected.The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality.These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.

Optical generation,detection and non-destructive testing applications of terahertz waves

Optoelectronic terahertz generation and detection play a key role in the applications of non-destructive testing,which involves different areas such as physics,biological,material science,imaging,explosions detection,astronomy applications,semiconductor technology and superconductiong electronics. In this article,we present a reviewof the principle and performance of typical terahertz sources,detectors and non-destructive testing applications. On this basis,the newdevelopment and trends of terahertz radiation detectors are also discussed.

Multi-Energy Gamma-Ray Attenuations for Non-Destructive Detection of Hazardous Materials

We present a non-destructive method (NDM) to identify minute quantities of high atomic number (Z) elements in containers such as passenger baggage, goods carrying transport trucks, and environmental samples. This method relies on the fact that photon attenuation varies with its energy and properties of the absorbing medium. Low-energy gamma-ray intensity loss is sensitive to the atomic number of the absorbing medium, while that of higher-energies vary with the density of the medium. To verify the usefulness of this feature for NDM, we carried out simultaneous measurements of intensities of multiple gamma rays of energies 81 to 1408 keV emitted by sources 133Ba (half-life = 10.55 y) and 152Eu (half-life = 13.52 y). By this arrangement, we could detect minute quantities of lead and copper in a bulk medium from energy dependent gamma-ray attenuations. It seems that this method will offer a reliable, low-cost, low-maintenance alternative to X-ray or accelerator-based techniques for the NDM of high-Z materials such as mercury, lead, uranium, and transuranic elements etc.

RESEARCH AND PRACTICE ON NONDESTRUCTIVE FLAW DETECTION INSTALLATION FOR WIRE-CORE BELT

Electromagnetic self-induction theory and computer are adopted and study of online monitoring technique for wire-core belt is conducted, the study shows that there is direct proportion between distance Ⅰ of broken ends and output volt Ⅴ, when Ⅰ ≥60 mm, Ⅴ keeps constantly, the running speed v of wire-core belt has no big effect on output volt Ⅴ, there is inverse proportion between the height h from probe to the surface of the belt and output volt Ⅴ, when h≥30 mm, Ⅴ tends to be zero. Based on the test result, on-line monitoring installation is developed, the practice proved that the accuracy of broken wire monitoring can be above 95%, the monitoring accuracy of joint twitch can be 0 .04 Ⅴ/mm.

Flaw Detection Capability and Sensitivity in the Inspection of Nuclear Containment Liner and Shell Mock-Ups Utilizing a Magnetostrictive Sensor (MsS) Guided Wave UT Technique

This paper describes the results of a project on the inspection of visually inaccessible areas of nuclear containment liners and shells via the advanced Magnetostrictive sensor (MsS) Guided Wave (GW) nondestructive inspection technique. Full scale mockups that simulated shell and liner regions of interest in the containment of both a Pressurized Water Reactor (PWR) and Boiling Water Reactor (BWR) were constructed. Inspections were performed on the mock-ups in three stages to discern the signal attenuation caused by flaws and caused by concrete in the structures. The effect of concrete being in close proximity to the liner and shell was determined, and the capability to detect and size flaws via this GW technique was evaluated.

A fast and adaptive method for automatic weld defect detection in various real-time X-ray imaging systems

A first and effective method is proposed to detect weld deject adaptively in various Dypes of real-time X-ray images obtained in different conditions. After weld extraction and noise reduction, a proper template of median filter is used to estimate the weld background. After the weld background is subtracted from the original image, an adaptite threshold segmentation algorithm is proposed to obtain the binary image, and then the morphological close and open operation, labeling algorithm and fids＇e alarm eliminating algorithm are applied to pracess the binary image to obtain the defect, ct detection result. At last, a fast realization procedure jbr proposed method is developed. The proposed method is tested in real-time X-ray image,s obtairted in different X-ray imaging sutems. Experiment results show that the proposed method is effective to detect low contrast weld dejects with few .false alarms and is adaptive to various types of real-time X-ray imaging systems.

A novel 4D resolution imaging method for low and medium atomic number objects at the centimeter scale by coincidence detection technique of cosmic-ray muon and its secondary particles

The muon radiography imaging technique for high-atomic-number objects(Z)and large-volume objects via muon transmission imaging and muon multiple scattering imaging remains a popular topic in the field of radiation detection imaging.However,few imaging studies have been reported on low and medium Z objects at the centimeter scale.This paper presents an imaging system that consists of three layers of a position-sensitive detector and four plastic scintillation detectors.It acquires data by coincidence detection technique of cosmic-ray muon and its secondary particles.A 3D imaging algorithm based on the density of the coinciding muon trajectory was developed,and 4D imaging that takes the atomic number dimension into account by considering the secondary particle ratio information was achieved.The resultant reconstructed 3D images could distinguish between a series of cubes with 5-mm-side lengths and 2-mm-intervals.If the imaging time is more than 20 days,this method can distinguish intervals with a width of 1 mm.The 4D images can specify target objects with low,medium,and high Z values.

Current Trends and Perspectives of Detection and Location for Buried Non-Metallic Pipelines

Buried pipelines are an essential component of the urban infrastructure of modern cities.Traditional buried pipes are mainly made of metal materials.With the development of material science and technology in recent years,non-metallic pipes,such as plastic pipes,ceramic pipes,and concrete pipes,are increasingly taking the place of pipes made from metal in various pipeline networks such as water supply,drainage,heat,industry,oil,and gas.The location technologies for the location of the buried metal pipeline have become mature,but detection and location technologies for the non-metallic pipelines are still developing.In this paper,current trends and future perspectives of detection and location of buried non-metallic pipelines are summarized.Initially,this paper reviews and analyzes electromagnetic induction technologies,electromagnetic wave technologies,and other physics-based technologies.It then focuses on acoustic detection and location technologies,and finally introduces emerging technologies.Then the technical characteristics of each detection and location method have been compared,with their strengths and weaknesses identified.The current trends and future perspectives of each buried non-metallic pipeline detection and location technology have also been defined.Finally,some suggestions for the future development of buried non-metallic pipeline detection and location technologies are provided.

Statistic Learning-based Defect Detection for Twill Fabrics

Template matching methods have been widely utilized to detect fabric defects in textile quality control. In this paper, a novel approach is proposed to design a flexible classifier for distinguishing flaws from twill fabrics by statistically learning from the normal fabric texture. Statistical information of natural and normal texture of the fabric can be extracted via collecting and analyzing the gray image. On the basis of this, both judging threshold and template are acquired and updated adaptively in real-time according to the real textures of fabric, which promises more flexibility and universality. The algorithms are experimented with images of fault free and faulty textile samples.

Detection of Cracks in Aerospace Turbine Disks Using an Ultrasonic Phased Array C-scan Device

Crack detection in an aerospace turbine disk is essential for aircraft-quality detection.With the unique circular stepped structure and superalloy material properties of aerospace turbine disk,it is difficult for the traditional ultrasonic testing method to perform efficient and accurate testing.In this study,ultrasound phased array detection technology was applied to the non-destructive testing of aviation turbine disks:(i)A phased array ultrasonic c-scan device for detecting aerospace turbine disk cracks(PAUDA)was developed which consists of phased array ultrasonic,transducers,a computer,a displacement encoder,and a rotating scanner;(ii)The influence of the detection parameters include frequency,wave-type,and elements number of the ultrasonic phased array probe on the detection results on the near-surface and the far surface of the aerospace turbine disk is analyzed;(iii)Specimens with flat-bottom-hole(FBH)defects were scanned by the developed PAUDA and the results were analyzed and compared with the conventional single probe ultrasonic water immersion testing.The experiment shows that by using the ultrasonic phased array c-scan to scan the turbine disk the accuracy of the detection can be significantly improved which is of greater accuracy and higher efficiency than traditional immersion testing.

Higher-Order Statistics for Automatic Weld Defect Detection

Image processing and image analysis are the main aspects for obtaining information from digital image owing to the fact that this techniques give the desired details in most of the applications generally and Non-Destructive testing specifically. This paper presents a proposed method for the automatic detection of weld defects in radiographic images. Firstly, the radiographic images were enhanced using adaptive histogram equalization and are filtered using mean and wiener filters. Secondly, the welding area is selected from the radiography image. Thirdly, the Cepstral features are extracted from the Higher-Order Spectra (Bispectrum and Trispectrum). Finally, neural networks are used for feature matching. The proposed method is tested using 100 radiographic images in the presence of noise and image blurring. Results show that in spite of time consumption, the proposed method yields best results for the automatic detection of weld defects in radiography images when the features were extracted from the Trispectrum of the image.

Photodetection of the Quality on Internal Surface of Steel Pipe

In this paper,a photoelectric device is introduced,which is used in detecting the quality on internal surface of thin and long steel pipe.In this device,the CCTV lens is used for extract tile flaw information on internal surface of the pipe,and make IBM-PC/AT 486 computer as controlling and image processing system.By this instrument,the functions,such as the digital conversion of input information,image processing,classification of recognition and output display can be obtained.In the petroleum and chemical industry,by using this apparatus,we can detect the quality on internal surface of various metal pipes with real-time automatically.

基于改进YOLOv5s 的白酒瓶盖瑕疵检测

目的瓶装白酒生产过程中,瓶盖表面瑕疵会影响产品外观质量。针对白酒瓶盖表面瑕疵检测效率低和目标检测效果差的问题,提出一种基于YOLOv5s的改进算法DTS-YOLO。方法首先,在主干网络中引入可变形卷积,以提高模型对极端长宽比瑕疵的检测精度。其次,引入Transformer编码块,使网络聚焦于提取图像的全局信息。最后,在颈部网络构建C3SE-Lite模块,将C3模块嵌入SE注意力模块的同时引入Ghost卷积,减少参数量的同时,增强对瓶盖瑕疵的检测能力。结果实验结果表明,本文所提方法相较于基础网络,参数量减少了10%,平均精度均值达95%,平均检测速度达30帧/s。结论本文方法有效实现了白酒瓶盖表面瑕疵快速、准确地检测,可广泛应用于瓶装白酒生产过程中瓶盖表面检测。

超大规格风电用连铸圆坯的氢含量控制

针对直径大于等于800 mm的超大规格风电用连铸圆坯因氢含量高出现探伤不合缺陷的问题,通过检测原辅料水分、利用Hydris定氢仪探究全工序钢水氢含量的变化规律。结果表明,铸坯氢含量的主要来源是原辅料水分、炉气气氛潮湿、耐火材料不完全分解。为降低铸坯氢含量,提出了工艺改进措施:转炉吹炼8 min后应不加或加入水分含量较低的原辅料;LF精炼工序投入较少的原辅料;入VD工序前小渣量控制,破空后低速喂线,控制喂线数量及种类在较小范围;小火烘烤连铸中间包60 min,定期清理中间包透气孔。工艺优化后,VD到站钢水w(H)≤3.6×10^(-6),VD离站钢水w(H)≤0.77×10^(-6),连铸中间包首炉钢水w(H)≤2.0×10^(-6),第2炉w(H)≤1.6×10^(-6),连浇炉次w(H)≤1.1×10^(-6),成功降低了超大规格风电用连铸圆坯的氢含量,保证了成品锻件的质量。

船舶维修机械臂水下探伤系统稳定性分析

为了满足船舶远海航行时对于水下船体探伤的作业需求,基于自主设计的水下机械臂系统,在其末端执行机构安装水下摄像头,以代替潜水员下水进行探伤作业,并对搭建的水下维修机械臂探伤系统的操控稳定性进行了分析验证。首先,基于拉格朗日函数建立了水下机械臂探伤系统的动力学方程,并利用Fluent软件仿真计算出在不同流速下机械臂探伤系统各关节处所受的水阻力大小;然后,将计算所得各关节水阻力作为载荷添加至动力学仿真分析中,利用Matlab软件对水下维修机械臂探伤系统进行了动力学仿真,得到各关节在空气中和水下不同环境中的力矩,求得水阻力对各关节力矩的影响因子;最后,在空气中和水下两种环境中对机械臂探伤系统进行运动控制试验,实际的力矩反馈和流体模型的建立验证了水下维修机械臂探伤系统良好的操控稳定性,为运用于船舶远航实际水下探伤作业提供了数据和实验支持。

一种基于单轴向充磁永磁环励磁的钢丝绳无损检测方法

为提高钢丝绳缺陷检测能力,简化励磁结构,提出一种基于单个轴向充磁永磁环励磁的钢丝绳无损检测方法。通过分析钢丝绳缺陷在单个轴向充磁永磁环励磁方法下产生的漏磁场,得到该方法能通过漏磁场感知缺陷运动方向的结论。在此基础上,利用COMSOL软件对该励磁方式进行有限元建模,通过仿真计算缺陷处漏磁的分布,验证了结论的正确性,分析了影响漏磁场的因素,并结合霍尔元件性能参数,给出了传感器的最佳布置位置。最后,通过设计信号调理电路,结合3D打印实现传感器结构,完成了实验平台搭建。仿真结果表明,对于同一缺陷,基于该励磁方式产生的最大漏磁场明显大于传统方式。实验结果表明,对于10 mm粗的钢丝绳,当霍尔元件布置的提离值为3 mm和8 mm时,均能够通过缺陷处漏磁信号感知钢丝绳运动方向。

铸件X射线检测虚拟探伤图像生成方法

随着国防核心装备对零件的要求不断提高,铸件向结构复杂化方向发展,如大壁厚差、多交叉结构等,致使铸件内部缺陷的无损检测更困难。X射线探伤过程中,拍片依赖人工反复调整成像部位和参数,存在探伤部位易漏检、成像质量波动大等问题,亟需发展智能分区调参拍片技术。为此,本文提出了铸件X射线检测虚拟探伤图像生成方法。首先,建立了铸件坐标降维映射模型,实现了铸件三维坐标降维映射到成像板平面二维坐标。其次,建立了仿真成像参数拟合模型,生成了点光源式虚拟射线对铸件三维模型的仿真探伤图像。最后,开发了铸件X射线检测虚拟成像系统。该方法可为实际探伤提供优化的成像姿态和成像参数信息,为智能分区调参拍片技术提供理论支撑。