Nondestructive Testing Technology Research Articles

Ultrasonic Total Focusing Method Technology in Carbon Steel Butt Weld Application

Carbon steel butt welds are widely used in aircraft carrier, shipbuilding, railway, aerospace and other industries. As a new technology of ultrasonic non-destructive testing, Total Focusing Method (TFM) technology is a method of image reconstruction in which the value of each constituent datum of the image results from focused ultrasound. TFM may also be understood as a broad term encompassing a family of processing techniques for image reconstruction from full matrix capture (FMC). This paper compares resolution and focus between TFM and conventional phased array technology, by using phased array test block type B. The paper also introduces the testing system, including equipment, probe, wedge, calibration block as per ASME, PC analysis software, for detection of 21mm thickness carbon steel welds. In order to improve the defect detection ability, a customized software on board was designed based on TFM technology, and an appropriate propagation mode was adopted for different types of defects, such as longitudinal cracks on surface, lack of fusion, porosities, slag inclusions. The test results showing that compared with conventional phased array technology, the defect information such as length and depth detected by TFM are nearer to the actual situation, and the shape is closer to the physical one. TFM technology has more advantages in quantitative and qualitative capabilities in testing carbon steel butt welds, and can meet the requirements of on-site testing.

Study on non-destructive visualization identification of concrete internal cracks based on SH array ultrasound

Ultrasonic nondestructive testing (NDT) technology was one of the most frequently used and fastest-developing NDT techniques in detection field. This method had high detection sensitivity, more accurate defect localization, and was harmless to the human body. With the development of ultrasonic testing technology, modern ultrasonic imaging technology generally had automatic data acquisition and processing functions, which allowed for intuitive and clear recognition of internal defects by the human eyes. The overall purpose of this study was to visualize the cracks in concrete by synthetic aperture focusing technique (SAFT) and full-waveform inversion (FWI) algorithms on the basis of ultrasonic nondestructive testing technology. At present, there have been studies on the use of ultrasonic instruments to detect the internal defects of concrete. However, the accuracy and authenticity of this method have not been compared in previous studies. Therefore, in this experiment, four concrete specimens with different buried depths, angles, shapes, and thicknesses of cracks were designed. Based on SH ultrasonic wave NDT technology, two types of operations were performed on the collected ultrasonic wave data: one was the application of SAFT method, and the other was the first application of FWI method for detecting internal cracks in concrete. The study found that both algorithms were most accurate in determining the burial depth of cracks, with error results not exceeding 10 %. They also showed good recognition effects for the angle, shape, and thickness of the buried cracks. This proved that both identification methods had the capability to recognize internal cracks. The Introview software bundled with the ultrasonic instrument could identify cracks using SAFT directly and quickly. When FWI was used with MATLAB computation, the results were more precise and intuitive. The overall purpose of this study was to detect cracks in concrete by SAFT and FWI algorithms on the basis of ultrasonic nondestructive testing technology. Combining the two crack identification techniques could be of great significance for ensuring the long-term safety and stability of concrete structures, providing strong support for structural maintenance and reinforcement.

Integration of Non-Destructive Testing Technologies for Effective Monitoring and Evaluation of Road Pavements

Integration of Non-Destructive Testing Technologies for Effective Monitoring and Evaluation of Road Pavements

Research on Pipeline Stress Detection Method Based on Double Magnetic Coupling Technology.

Oil and gas pipelines are subject to soil corrosion and medium pressure factors, resulting in stress concentration and pipe rupture and explosion. Non-destructive testing technology can identify the stress concentration and defect corrosion area of the pipeline to ensure the safety of pipeline transportation. In view of the problem that the traditional pipeline inspection cannot identify the stress signal at the defect, this paper proposes a detection method using strong and weak magnetic coupling technology. Based on the traditional J-A (Jiles-Atherton) model, the pinning coefficient is optimized and the stress demagnetization factor is added to establish the defect of the ferromagnetic material. The force-magnetic relationship optimization model is used to calculate the best detection magnetic field strength. The force-magnetic coupling simulation of Q235 steel material is carried out by ANSYS 2019 R1 software based on the improved J-A force-magnetic model. The results show that the effect of the stress on the pipe on the magnetic induction increases first and then decreases with the increase in the excitation magnetic field strength, and the magnetic signal has the maximum proportion of the stress signal during the excitation process; the magnetic induction at the pipe defect increases linearly with the increase in the stress trend. Through the strong and weak magnetic scanning detection of cracked pipeline materials, the correctness of the theoretical analysis and the validity of the engineering application of the strong and weak magnetic detection method are verified.

Application Study of Acoustic Reflectivity Based on Phased Array Ultrasonics in Evaluating Lubricating Oil Film Thickness

Bearings play a key role in rolling mills, and the uniformity of their lubricant film directly affects the degree of wear of bearings and the safety of equipment. Due to long-term stress, the lubricant film inside the bearing is not uniformly distributed, resulting in uneven wear between the journal and the shaft tile, which increases the potential safety hazards in production. Traditional disassembly inspection methods are complex and time-consuming. Ultrasonic nondestructive testing technology, which has the advantages of nondestructive and adaptable, has become an effective means of assessing the thickness of the oil film in bearings. In this study, an experimental platform for calibrating the lubricant film thickness in bearings was constructed for the first time, and the acoustic characteristics of different thicknesses of the oil film were measured using ultrasonic detection equipment to verify the accuracy of the simulation process. The experimental results show that after discrete Fourier transform processing, the main features of the frequency channels of the reflected acoustic signals of different thicknesses of the oil film are consistent with the finite element simulation results, and the errors of the oil film thicknesses calculated from the reflection coefficients are within 10% of the set thicknesses, and the measurement ranges cover from 5 μm to 250 μm. Therefore, the above method can realize the accurate measurement of the thicknesses of the oil film in bearings.

Study of the Martensitic Transformation of Steel During the Induction Hardening Process by means of Acoustic Emissions

Non-Destructive Testing (NDT) technology known as Acoustic Emission (AE) has been used to investigate the phase transformation of 42CrMo4 steel in a cylinder during an induction hardening process. The objective of this study is to characterize the martensitic transformation of the material using the data obtained by the AE sensor during the process. The heating process of the cylinder is carried out statically, focusing the heat on the area to be treated and then analyzing it. After quenching, destructive tests are carried out to determine the hardness and three-dimensional geometry of the steel microstructure after the process (hardness and X-ray diffraction tests). Once the microstructure of the material is known, it is possible to relate the results with the signals obtained by AE, making possible in future induction hardening processes the estimation of martensite by non-destructive techniques.

Hierarchical robot learning method for industrial fluorescent penetrant inspection

Fluorescent Penetrant Inspection (FPI) is a Non-Destructive Testing (NDT) method, extensively used to evaluate components for identifying defects across a broad range of industries. FPI process remains a manual visual inspection, where the operator by means of fluorescent dye, that penetrates discontinuities on the component, aims to distinguish between indications that are relevant (i.e., can be associated with surface defects) and non-relevant (i.e., can be associated to insufficient wash-off, dust or other non-relevant factors). The FPI process can be decomposed into the following steps: (a) thorough visual examination of the component, (b) executing manual wiping-off of the fluorescent dye with a brush, of all areas that require interrogation for potential indications, and (c) disposition of the inspected components. The number of those areas on the component that require interrogation and hence to be wiped-off is unknown a priori of the inspection and varies depending on the condition of the part. As a result, replacing this manual wipe-off step by a robot requires tedious manual programming of an excessive number of robot paths to assure reach of the robot to the entire surface of the part as well as safe robot motion. In addition, these robot motions are part specific and thus not transferrable to other geometries of components, making scaling of this technology across manufacturing industry not possible. In this paper, we propose a hierarchical robot learning method to address the challenge of reducing manual robot programming and enable the scaling of this automated NDT technology. The proposed method integrates and fuses Deep Reinforcement Learning (DRL), Screw Linear Interpolation (ScLERP) and Learning from Demonstration (LfD), enabling an autonomous generation of brushing strokes with a six-degrees of freedom (DoF) industrial manipulator, and automating the wipe-off step of the FPI process. Using this approach, a robot learning policy is generated for the wipe-off motion in a simulated industrial robotic cell at first and then the policy is transferred to the real system for validation.

Estimation Model for Maize Multi-Components Based on Hyperspectral Data.

Assessing the quality of corn seeds necessitates evaluating their water, fat, protein, and starch content. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498 nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using the original spectra and extracted characteristic wavelengths, PLSR, BP, RBF, and LSSVM models were constructed to detect the content of four components. The analysis indicated that the CARS-LSSVM algorithm had the best prediction performance. The PSO algorithm was employed to further optimize the parameters of the LSSVM model, thereby improving the model's prediction performance. The R values for the four components in the test set were 0.9884, 0.9490, 0.9864, and 0.9687, respectively. This indicates that hyperspectral technology combined with the DT-CARS-PSO-LSSVM algorithm can effectively detect the main component content of corn seeds. This study not only provides a scientific basis for the evaluation of corn seed quality but also opens up new avenues for the development of non-destructive testing technology in related fields.

Circumferential multi-point corrosion status assessment of steel cables considering self-magnetic flux leakage superposition effect

With the advantages of convenient operation and efficient detection, the self-magnetic flux leakage (SMFL) has become an important technology for non-destructive testing of ferromagnetic materials. However, the multi-point corrosion status of steel cable structures is still difficult to precisely assess. In this study, the SMFL theoretical model for the circumferential distribution of steel cable corrosion was established, and the measurement experiment of circumferential multi-point corrosion status was carried out. The results show that the area of circumferential distribution region S, the accumulated magnetic field intensity A and ellipticity e are all positively and quadratically correlated with the corrosion ratio α and defect angle θ of steel cables. Moreover, the magnetic field superposition effect shows an exponential decay trend with the defect interval angle Δφ. The maximum deviation rate of A was less than 10 % under different multi-point corrosion distributions, verifying the principle of magnetic field superposition. Finally, the assessment method based on the effect was proposed, which could accurately and systematically diagnose the circumferential multi-point corrosion status in steel cables.

Application of Object Detection Algorithms in Non-Destructive Testing of Pressure Equipment: A Review.

Non-destructive testing (NDT) techniques play a crucial role in industrial production, aerospace, healthcare, and the inspection of special equipment, serving as an indispensable part of assessing the safety condition of pressure equipment. Among these, the analysis of NDT data stands as a critical link in evaluating equipment safety. In recent years, object detection techniques have gradually been applied to the analysis of NDT data in pressure equipment inspection, yielding significant results. This paper comprehensively reviews the current applications and development trends of object detection algorithms in NDT technology for pressure-bearing equipment, focusing on algorithm selection, data augmentation, and intelligent defect recognition based on object detection algorithms. Additionally, it explores open research challenges of integrating GAN-based data augmentation and unsupervised learning to further enhance the intelligent application and performance of object detection technology in NDT for pressure-bearing equipment while discussing techniques and methods to improve the interpretability of deep learning models. Finally, by summarizing current research and offering insights for future directions, this paper aims to provide researchers and engineers with a comprehensive perspective to advance the application and development of object detection technology in NDT for pressure-bearing equipment.

Evaluation of 3D printed 316L stainless steel microstructure and mechanical property by laser ultrasonics

ABSTRACT In this paper, the laser ultrasonic non-destructive testing technology was used to detect the microstructure and mechanical properties of selective laser melting (SLM) 316 L stainless steel after post-treatment. The microstructure and mechanical properties of additively manufactured 316 L stainless steel were measured by Scanning electron microscopy (SEM), Electron backscatter diffraction (EBSD), tensile testing, etc. The laser ultrasonic longitudinal wave signal characteristic values were coupled with the microstructure and mechanical properties of SLM 316 L stainless steel. The results show that: The yield strength has a good correlation with wave speed and attenuation. With the increase of holding temperature and holding time, the grain size increases, the low angle grain boundary decreases, the yield strength gradually decreases from 409 MPa to 359 MPa, the wave velocity increases from 5579 m/s to 5700 m/s, and the frequency domain attenuation coefficient increases from 0.13 dB/mm to 0.16 dB/mm, the central frequency domain is about 10 MHz. The ultimate tensile strength is related to the frequency domain attenuation. The ultimate tensile strength decreases with the increase of the frequency domain attenuation coefficient. It can provide a reference for the evaluation of mechanical properties by laser ultrasonic testing.

WD-1D-VGG19-FEA: An Efficient Wood Defect Elastic Modulus Predictive Model.

As a mature non-destructive testing technology, near-infrared (NIR) spectroscopy can effectively identify and distinguish the structural characteristics of wood. The Wood Defect One-Dimensional Visual Geometry Group 19-Finite Element Analysis (WD-1D-VGG19-FEA) algorithm is used in this study. 1D-VGG19 classifies the near-infrared spectroscopy data to determine the knot area, fiber deviation area, transition area, and net wood area of the solid wood board surface and generates a two-dimensional image of the board surface through inversion. Then, the nonlinear three-dimensional model of wood with defects was established by using the inverse image, and the finite element analysis was carried out to predict the elastic modulus of wood. In the experiment, 270 points were selected from each of the four regions of the wood, totaling 1080 sets of near-infrared data, and the 1D-VGG19 model was used for classification. The results showed that the identification accuracy of the knot area was 95.1%, the fiber deviation area was 92.7%, the transition area was 90.2%, the net wood area was 100%, and the average accuracy was 94.5%. The error range of the elastic modulus prediction of the three-dimensional model established by the VGG19 classification model in the finite element analysis is between 2% and 10%, the root mean square error (RMSE) is about 598. 2, and the coefficient of determination (R2) is 0. 91. This study shows that the combination of the VGG19 algorithm and finite element analysis can accurately describe the nonlinear defect morphology of wood, thus establishing a more accurate prediction model of wood mechanical properties to maximize the use of wood mechanical properties.

Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning

Drilling risers play a crucial role in deepwater oil and gas development, and any compromise in their integrity can severely hinder the progress of drilling operations. In light of this, efficient and accurate nondestructive testing of drilling risers is paramount. However, existing inspection equipment falls short in both efficiency and accuracy, posing challenges to the sustainability of deepwater oil and gas exploration and development. To effectively assess the damage conditions of deepwater drilling risers, this study developed an inspection robot based on metal magnetic memory and researched intelligent defect recognition methods using computer vision. The robot can perform in situ inspections on drilling risers and has been successfully deployed for field application on a deepwater drilling platform. The application results demonstrate that this detection robot offers significant advantages regarding high reliability and detection efficiency. Utilizing data collected on-site, we constructed a dataset containing 1100 images that cover five typical types of defects in drilling risers, including pitting, groove corrosion, and wear. Based on this dataset, we proposed and trained a novel image classification model, SK-ConvNeXt-KAN. By deeply optimizing the ConvNeXt convolutional network incorporating the introduced SK attention module and replacing traditional linear classification layers with the KAN module, this model significantly enhanced its feature extraction capabilities and efficiency in handling complex nonlinear problems. Experimental results show that this model achieved an accuracy rate of 95.4% in identifying defects in drilling risers, which is significantly better than traditional methods. This achievement has dramatically improved the efficiency and accuracy of deepwater drilling riser inspections, providing robust technical support for deepwater oil and gas exploration and development sustainability.

基于透射光谱技术的马铃薯种薯黑心病检测研究

Black heart disease is one of the screening indicators of seed potatoes, which has a serious impact on the quality and yield of potato, and at present there are fewer non-destructive testing methods for internal defects of seed potatoes. This paper aims to utilize non-destructive testing technology to quickly identify qualified and black hearted seed potatoes, and then to protect yield and food security. In this paper, transmission spectroscopy system was utilized to collect the spectral data of 104 qualified seed potatoes and 104 black hearted seed potatoes in 450~940 nm band. Subsequently, four algorithms, namely Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and First-order Derivative (FD), were utilized to pre-process the seed potatoes spectral data to improve the data noise ratio. Feature wavelength extraction was made using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) to enhance the sample data characteristics and improve the model interpretability. The construction of classification models for qualified and black hearted seed potatoes relied on two deep learning techniques, Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), which were trained and tested for the feature bands respectively. The experimental results showed that SG-CARS-CNN was the optimal combination of classification algorithms, and the classification accuracies of both the training set and the test set reached 100%, which improved the accuracy of the test set by 3.85% compared with that of the traditional machine learning algorithms, and provided an accurate method for the rapid screening of qualified seed potatoes.

Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD

Defects in welds can affect the structural safety and reliability of workpieces. Currently, the method of using phased array ultrasonic inspection technology for non-destructive testing of weld structures with high detection efficiency, good sensitivity, and good visualization of the results is widely used. However, the defective A-scan data collected by the ultrasonic phased array detector inevitably contain noise data, including the test piece material structure noise, equipment noise, and environmental noise, which undoubtedly affects the analysis of the A-scan signal. In addition, when defects are interpreted, the presence of noise also interferes with the process, which affects the accuracy of the interpretation. Therefore, to enhance the accuracy of defect identification based on phased array ultrasonic inspection technology, we must prevent the series of consequences caused by misjudgments. In this study, ultrasonic phased array inspection experiments were carried out, and the specific process flow of ultrasonic phased array inspection of flat plate butt welds was summarized. Utilizing pre-fabricated flat plate butt specimen blocks containing five types of typical defects, defect A-sweep signals based on ultrasonic phased array inspection were obtained. Combining the sparrow optimization algorithm (SSA), variational mode decomposition (VMD), and wavelet packet decomposition (WPD), a defect signal noise reduction method based on parameter optimization was studied. A noise reduction study was carried out using the noise-added simulated signal, and the results indicated that the noise reduction method proposed in this paper had a better noise reduction effect and the proposed method could effectively retain the detailed features of the ultrasonic phased array defective A-scan signal and realize the noise reduction processing of the defective A-scan signal.

Nutritional Quality Analysis and Classification Detection of Buckwheat in Different Harvest Periods.

For buckwheat, the optimal harvest period is difficult to determine-too early or too late a harvest affects the nutritional quality of buckwheat. In this paper, physical and chemical tests are combined with a method using near-infrared spectroscopy nondestructive testing technology to study buckwheat harvest and determine the optimal harvest period. Physical and chemical tests to determine the growth cycle were performed at 83 days, 90 days, 93 days, 96 days, 99 days, and 102 days, in which the buckwheat grain starch, fat, protein, total flavonoid, and total phenol contents were assessed. Spectral images of buckwheat in six different harvest periods were collected using a near-infrared spectral imaging system. Four preprocessing methods (SNV, S-G, DWT, and the normaliz function) and three dimensionality reduction algorithms (IVSO, VCPA, VISSA) were used to process the raw buckwheat spectral data, and the full and eigen spectra were established as a random forest (RF). Random forest (RF) and Least Squares Support Vector Machine (LS-SVM) classification models were used to determine the full and eigen spectra, respectively, and the optimal model for the buckwheat single harvest period was determined and validated. Through physical and chemical tests, it was concluded that the 90-day harvest buckwheat grain protein, fat, and starch contents were the highest, and that the total flavonoid and total phenolic contents were also high. The SNV preprocessing method was the most effective, and the feature bands extracted using the IVSO algorithm were more representative. The IVSO-RF model was the best discriminative model for the classification of buckwheat in different harvest periods, with the correct rates of the training and prediction sets reaching 100% and 96.67%, respectively. When applying the IVSO-RF model to the buckwheat single harvest period to verify the classification, the correct rate of the training set for each harvest period reached 96%, and that of the prediction set reached 100%. Near-infrared spectroscopy combined with the IVSO-RF modeling method for buckwheat harvest period detection is a rapid, nondestructive classification method. When this was combined with physical and chemical analyses, it was determined that a growth cycle of 90 days is the best harvest period for buckwheat. The results of this study can not only improve the quality of buckwheat crops but also be applied to other crops to determine their optimal harvest period.

Research on the intelligent fatigue detection of metal components in vehicles

With the acceleration of transportation speed, heavy loading of trucks has higher requirements for the quality of wheel axles, and the detection of wheel axles on trucks has become a key focus in corrective maintenance. The automotive industry requires timely detection of vehicle components. Nondestructive testing (NDT) technology provides a method of detecting and analyzing physical quantities on the internal surface of an object without causing damage. It can determine the presence of defects or abnormal conditions. The application of nondestructive testing (NDT) is essential in the automotive industry to guarantee the safety and dependability of vehicles. This article provides an overview of the principles and applications of phased array ultrasound imaging and eddy current testing, focusing on intelligent monitoring solutions for axles. This article proposes an enhanced intelligent monitoring approach that utilizes existing detection methodologies to provide vehicle owners with real-time feedback regarding the fatigue strength of individual automotive parts. This approach aims to prompt the replacement of auto parts when the fatigue trend approaches predetermined warning thresholds.

Non-destructive testing technology for corrosion wall thickness reduction defects in pipelines based on electromagnetic ultrasound

Non-destructive testing technology for corrosion wall thickness reduction defects in pipelines based on electromagnetic ultrasound

Method for Predicting Internal Defect Areas of Timber Components Based on an Adaptive Neuro-Fuzzy Inference System

ABSTRACT Internal defects in the timber components of ancient architecture can reduce their effective cross-sectional areas, deteriorate their mechanical performance, and increase the risk of structural failure. To accurately and rapidly determine the extent of internal damage in components of ancient architecture, this study performed damage detection tests on Larix gmelinii (Larch) column specimens using stress wave nondestructive testing technology. An adaptive neuro-fuzzy inference system (ANFIS) model was developed using the cross-sectional wave velocity ratio after investigating the laws of defect identification through stress-wave testing. Stress-wave detection could identify the relative positional correlation of defect areas. However, the accuracy of the defect area detection was inconsistent (8%–98%). The accuracy of the ANFIS model exhibits a diminishing trend as both parameters rejection rate and influence range increase. However, the ANFIS outperformed the stress-wave detection technique and linear regression model with correlation coefficient and prediction accuracy of 0.99 and 88%, respectively. The root mean square error for the ANFIS was reduced by 79% and 70% compared to the stress wave detection method and linear regression model, respectively. Thus, the ANFIS model can precisely forecast areas of internal damage in timber components of historical edifices and can be used to evaluate the safety of ancient timber buildings.

Weld Defect Inspection of Cylindrical Secondary Batteries Based on Active Thermographic Measurement with Thermal Magnification

This paper presents an active thermographic measurement-based inspection tech-nique for detecting weld defects in the battery cap region of cylindrical secondary batteries. As the proliferation of electric vehicles continues, safety issues related to secondary batteries have become prominent. In this study, a defect detection algorithm was developed by aligning visual images and active thermographic im-ages of the cylindrical secondary battery cap region. Lock-in amplitude images during the heating-cooling process using a laser were extracted to measure the difference in temperature patterns arising from internal structural variations. Ad-ditionally, to minimize the influence of surface conditions and temperature varia-tions other than laser-induced, a 10Hz periodic heating was employed, and ther-mal magnification in the heating frequency was achieved through data processing. This approach allowed the measurement of temperature changes induced by laser heating and cooling while minimizing the impact of surface conditions and tem-perature variations unrelated to the laser. A dataset of 1000 specimens was con-structed by aligning thermographic data with vision images acquired through this method. defective samples. Using this thermographic-vision alignment dataset, a defect detection algorithm was trained to achieve a more accurate and faster in-spection performance compared to existing non-destructive testing technologies.