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.

DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.

Welding deviation detection method based on weld pool image contour features

Automatic on-line detection of welding deviation based on machine vision is one of the key technologies of arc welding robot tracking welding,in which obtaining high quality weld pool image and accurate welding deviation detection algorithm are two important steps of tracking welding.Through the research and analysis of the weld pool image of gas metal arc welding(GMAW),it was found that the weld pool contains abundant welding information.First,the average gray value of the weld pool image can reflect the interference degree of arc to weld pool image and the heat input of welding process.Secondly,the tip of the weld pool image contour can reflect the center of the groove gap.Finally,the horizontal distance between the center coordinate of the wire contour and the tip coordinate of the weld pool image contour can reflect the welding deviation.On the basis of analyzing the characteristics of weld pool image,this paper proposes a new method of weld seam deviation detection,which includes the collection of weld pool image,image preprocessing,contour extraction and deviation calculation.The results of the tests and analyses showed that the maximum error of the on-line welding deviation obtained was about 2 pixels(0.17 mm)when the welding speed was≤60 cm/min,and the algorithm was stable enough to meet the requirements of real-time deviation detection for I-groove butt welding.The method can be applied to the on-line automatic welding deviation detection of arc welding robot.

Vision-based detection of weld pool width in TIG welding of copper-clad aluminum cable

In order to realize automatic control of the width of weld pool, a visual sensor system for the width of weld pool detection is developed. By initiative arc light, the image of copper plate weld pool is taken back of the torch through the process of weakening and filtering arc light. In order to decrease the time of processing video signals, analog circuit is applied in the processing where video signals is magnified, trimmed and processed into binary on the datum of dynamic average value, therefore the waveform of video signals of weld pool is obtained. The method that is used for detecting the width of weld pool is established. Results show that the vision sensing method for real-time detecting weld pool width to copper-clad aluminum wire TIG welding is feasible. The response cycle of this system is no more than 50 ms, and the testing precision is less than 0. 1 mm.

Realizing precision pulse TIG welding with arc length control and visual image sensing based weld detection

Methods of arc length control and visual image based weld detection for precision pulse TIG welding were investigated. With a particular all hardware circuit, arc voltage during peak current stage is sampled and integrated to indicate arc length, deviation of arc length and adjusting parameters are calculated and output to drive a step motor directly. According to the features of welding image grabbed with CCD camera, a special algorithm was developed to detect the central line of weld fast and accurately. Then an application system were established, whose static arc length error is ±0.1 mm with 20 A average current and 1 mm given arc length, static detection precision of weld is 0.01 mm , processing time of each image is less than 120 ms . Precision pulse TIG welding of some given thin stainless steel components with complicated curved surface was successfully realized.

DETECTION METHOD OF SPOT WELDING BASED ON MULTI-INFORMATION FUSION AND FRACTAL

A novel detection method of support vector machine （SVM） based on fractal dimension of signals is presented. And models of SVM are made based on nugget size defects of spot welding. Classification using these trained SVM models is done to signals of spot welding. It is shown from effect of different SVM models that these models with different inputs. In detection of defects, these models with inputs including sound signal have a high percentage of accuracy, the detection accuracy of these models with inputs including voltage signal will reduce. So the SVM models based on fractal dimensions of sound are some optimal nondestructive detection ones. At last a comparison between SVM detection model and ANNS detection model is researched which indicates that SVM is a more effective measure than Artificial neural networks in detection of nugget size defects during spot welding.

Edge detection of molten pool and weld line for CO_2 welding based on B-spline wavelet

Due to the disturbances of spatters, dusts and strong arc light, it is difficult to detect the molten pool edge and the weld line location in CO_2 welding processes. The median filtering and self-multiplication was employed to preprocess the image of the CO_2 welding in order to detect effectively the edge of molten pool and the location of weld line. The B-spline wavelet algorithm has been investigated, the influence of different scales and thresholds on the results of the edge detection have been compared and analyzed. The experimental results show that better performance to extract the edge of the molten pool and the location of weld line can be obtained by using the B-spline wavelet transform. The proposed edge detection approach can be further applied to the control of molten depth and the seam tracking.

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.

Welding anomaly detection based on supervised learning and unsupervised learning

In order to solve the problem of automatic defect detection and process control in the welding and arc additive process,the paper monitors the current,voltage,audio,and other data during the welding process and extracts the minimum value,standard deviation,deviation from the voltage and current data.It extracts spectral features such as root mean square,spectral centroid,and zero-crossing rate from audio data,fuses the features extracted from multiple sensor signals,and establishes multiple machine learning supervised and unsupervised models.They are used to detect abnormalities in the welding process.The experimental results show that the established multiple machine learning models have high accuracy,among which the supervised learning model,the balanced accuracy of Ada boost is 0.957,and the unsupervised learning model Isolation Forest has a balanced accuracy of 0.909.

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.

Detection of butt weld of laser-MIG hybrid welding of thin-walled profile for high-speed train

Purpose–This study aims to solve the problem of weld quality inspection,for the aluminum alloy profile welding structure of high-speed train body has complex internal shape and thin plate thickness(2–4 mm),the conventional nondestructive testing method of weld quality is difficult to implement.Design/methodology/approach–In order to solve this problem,the ultrasonic creeping wave detection technology was proposed.The impact of the profile structure on the creeping wave detection was studied by designing profile structural test blocks and artificial simulation defect test blocks.The detection technology was used to test the actual welded test blocks,and compared with the results of X-ray test and destructive test(tensile test)to verify the accuracy of the ultrasonic creeping wave test results.Findings–It is indicated that that X-ray has better effect on the inspection of porosities and incomplete penetration defects.However,due to special detection method and protection,the detection speed is slow,which cannot meet the requirements of field inspection of the welding structure of aluminum alloy thin-walled profile for high-speed train body.It can be used as an auxiliary detection method for a small number of sampling inspection.The ultrasonic creeping wave can be used to detect the incomplete penetration welds with the equivalent of 0.25 mm or more,the results of creeping wave detection correspond well with the actual incomplete penetration defects.Originality/value–The results show that creeping wave detection results correspond well with the actual non-penetration defects and can be used for welding quality inspection of aluminum alloy thin-wall profile composite welding joints.It is recommended to use the echo amplitude of the 10 mm 30.2 mm 30.5 mm notch as the criterion for weld qualification.

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.

Vision-based detection of MAG weld pool

Weld pool contains significant information about the welding process. The weld pool images of MAG welding are detected by LaserStrobe system. An algorithm for extracting weld pool edge is proposed according to the characteristics of MAG weld pool images. The maximum weld pool length and width are calculated. The measurement data can be used to verify the results of welding process simulation and to provide a good foundation for automatic control of MAG welding process.

Edge detection and its application to recognition of arc weld image

Abstract Image sensor has been one of the key technologies in intellectualized robotics welding. Edge detection plays an important role when the vision technology is applied in intellectualized welding robotics technologies. There are all kinds of noises in welding environment. The algorithms in common use cannot be applied to the recognition of welding environment directly. The edge of images can be fell into four types. The weld images are classified by the characteristic of welding environment in this paper. This paper analyzes some algorithms of edge detection according to the character of welding image, some relative advantages and disadvantages are pointed out when these algorithms are used in this field, and some suggestions are given. The feature extraction of weld seam and weld pool are two typical problems in the realization of intellectualized welding. Their edge features are extracted and the results show the applicability of different edge detectors. The trndeoff between precision and calculated time is also considered for different application.

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.

Application of Support Vector Machine in Weld Defect Detection and Recognition of X-ray Images

Support vector machines（SVM） received wide attention for its excellent ability to learn, it has been applied in many fields. A review of the application of SVM in weld defect detection and recognition of X-ray image is been presented. We will show some commonly used methods of weld defect detection and recognition using SVM, and the advantages and disadvantages of each method will be discussed. SVM appears to be promising in weld defect detection and recognition, but future research is needed before it fully mature in this filed.

An artificial neural network for detecting weld position in arc welding process

A kind of self organizing artificial neural network used for weld detection is presented in this paper, and its concepts and issues are discussed. The network can transform the weld visual information into typical patterns and match with the weld data collected on line, and so realize the accurate detection of the weld position in arc welding process.

BACKGROUND RECTIFICATION AND FEATURE EXTRACTION OF IMAGE IN A SPOT WELD OF AL ALLOY X-RAY DETECTION

A primary study on Processing in X - ray inspection of spot weld for aluminum alloy spot welding,in- cluding for background simulation,acquisition of ideal binary image, and extraction and identifi- cation of defect features was presented.