Non-destructive Testing Equipment Research Articles

Non-destructive detection of defective maize kernels using hyperspectral imaging and convolutional neural network with attention module

Rapid, effective and non-destructive detection of the defective maize kernels is crucial for their high-quality storage in granary. Hyperspectral imaging (HSI) coupled with convolutional neural network (CNN) based on spectral and spatial attention (Spl-Spal-At) module was proposed for identifying the different types of maize kernels. The HSI data within 380–1000 nm of six classes of sprouted, heat-damaged, insect-damaged, moldy, broken and healthy kernels was collected. The CNN-Spl-At, CNN-Spal-At and CNN-Spl-Spal-At models were established based on the spectra, images and their fusion features as inputs for the recognition of different kernels. Further compared the performances of proposed models and conventional models were built by support vector machine (SVM) and extreme learning machine (ELM). The results indicated that the recognition ability of CNN with attention series models was significantly better than that of SVM and ELM models and fused features were more conducive to expressing the appearance of different kernels than single features. And the CNN-Spl-Spal-At model had an optimal recognition result with high average classification accuracy of 98.04 % and 94.56 % for the training and testing sets, respectively. The recognition results were visually presented on the surface image of kernels with different colors. The CNN-Spl-Spal-At model was built in this study could effectively detect defective maize kernels, and it also had great potential to provide the analysis approaches for the development of non-destructive testing equipment based on HSI technique for maize quality.

Mechanical Analysis of Semi-Rigid Base Asphalt Pavement under the Influence of Groundwater with the Spectral Element Method

Over prolonged exposure to groundwater conditions, semi-rigid base asphalt pavements can undergo significant changes in their internal moisture field, resulting in substantial variations in the pavement’s stiffness and, consequently, affecting the overall load-bearing capacity and stability of the road structure. This paper employs FWD non-destructive testing equipment to assess its mechanical performance and conduct data analysis and conducts a mechanical response study of asphalt road surfaces, considering the influence of roadbed moisture levels. Using the dynamic analytical theory, the fundamental equations and stiffness matrices for a linear elastic half-space model were established, leading to the development of a computational model for the mechanical response of semi-rigid base asphalt pavements under FWD dynamic loading, with an examination of the surface deflection in relation to changes in groundwater levels. Numerical examples and engineering applications were employed to validate the proposed model. The research findings indicate: With the passage of time, surface deflection values initially increase and then decrease, exhibiting a sinusoidal variation pattern similar to that of the applied load. As the distance from the loading center increases, the moment of peak deflection continually lags behind. The average absolute relative error between the results obtained using the method proposed in this study and the traditional ABAQUS finite element method was only 0.70%. The correlation coefficient between the theoretically computed deflection curve and the measured deflection curve using the spectral element method was greater than 0.9, with an average absolute relative error of 4.92% between the theoretical peak deflection and the measured peak deflection. As the groundwater level rises, surface deflection noticeably increases, with an approximately 40% increase in deflection values at the loading center. These research findings can be utilized to analyze the dynamic deflection of semi-rigid base asphalt pavements under various groundwater conditions, providing significant practical value for assessing road structural performance and serviceability.

Development of Technology and Equipment for Non-destructive Testing of Defects in Sewing Mandrels of a Three-roll Screw Mill 30-80

The conditions of heating and cooling of piercing mandrels made of 4X5MFS steel of a three-roll screw mill 30-80 in the production of a closed cavity of steel vessels of small volume are determined. It is established that multiple cycles of heating up to 600 °C and cooling with water up to 80 °C for about 7 seconds/1 cycle lead to the formation of ridges, shells and cracks on the surface and in the volume of the tool. The loss of structural strength of the material leads to the breakdown of the mandrel during the stitching process. The technique and equipment of magnetic powder control have been developed to establish the dynamics of the growth of internal and external defects of mandrels. An equation is obtained that allows determining the increase in the number of defects in the sewing tool of a screw rolling mill. The technology of non-destructive testing made it possible to develop a rational plan for replacing the sewing mandrels, which allows for predicting the appearance of defects leading to a complex breakdown of the deforming tool at the NPO Pribor machine-building enterprise.

Using Random Forest Algorithm to Grading Mango's Quality Based on External Features Extracted from Captured Images

The grading of mango is still a manual process in agriculture. Nowadays, mangoes are classified based on human experience, which makes the grade not uniform for agricultural product export establishments. Therefore, the automated grading of mango is very important to solve these problems. In this study, a random forest algorithm is proposed for an automated mango grading system based on quality attributes such as density, surface defect, and weight. The internal features including dimensions and surface defects are extracted via the captured image. These features are combined with the weight to estimate density. This study uses 732 mangoes that are collected from several local farms. The experiment of the grading system has high accuracy with 98.3%. Instead of using Non-Destructive Testing (NDT) equipment, this grading method can be used to apply to evaluate the quality of other tropical fruits.

Controlled seeding of contact cracks in test pieces for the evaluation and validation of non-destructive testing systems

Background: A major inhibitor in the development and certification of non-destructive test equipment is the lack of access to statistically appropriate numbers of test pieces which contain relevant flaws. Methods of introducing these flaws in a controllable and repeatable manner are always in demand. Methods: This paper discusses the development and evaluation of a method for seeding contact cracks in ASTM F519-18 Type 1a.1 notched tensile coupons. Contact cracks of controlled dimensions are successfully introduced into a set of these specimens by closely monitoring the resonance frequency and peak splitting due to anisotropy. Results: Results are confirmed using Scanning Electron Microscopy, carried out on a selection of the coupons. The least exposed coupon was shown to have a crack propagating inwards from the notch with a length of approximately 50 microns. More heavily exposed samples had longer cracks spanning nearly the entire notch with a crack width of 1.3 microns. Conclusions: The correlation between crack growth and the frequency monitoring proves that this technique could be further developed for the controlled creation of contact cracks for NDT validation or other applications.

Infrared image segmentation of cracks in the rail foot

With the rapid development of socio-economy, railway safety operation has attracted increasing attention. Currently, the most widely used crack detection method on railways is still ultrasonic testing. However, the traditional ultrasonic testing method has certain blind zones and cannot detect cracks on both sides of the rail foot. This paper uses infrared non-destructive testing equipment to detect the temperature distribution on the surface of the rail foot, obtains the infrared image of the temperature distribution on the surface of the rail foot, and uses iterative threshold segmentation method to segment the infrared image. The skeleton of rail cracks is extracted from the temperature information of the infrared image, and the length of rail cracks is calculated based on the coordinate temperature information at the edge of the rail. This improves the accuracy of calculating the geometric characteristics of rail foot cracks.

Ginsenoside Rg2 content prediction in Panax ginseng based on the fusion of hyperspectral wavelengths combined with chemometric analysis

The content of ginsenosides plays a crucial role in determining the quality of Panax ginseng C. A. Meyer. In order to clarify the potential applicability of hyperspectral imaging (HSI) for fast detecting the quality of ginseng, this study investigated the use of HSI combined with chemometric models to predict the ginsenoside Rg2 content. The results indicated that the prediction efficiency can be improved through complete wavelength fusion of different shots and the effective wavelength fusion obtained by different selection methods. Notably, the integrated utilization of the partial least squares regression (PLSR) algorithm and the fusion of effective wavelengths exhibited the best results in Rg2 content prediction, with the maximum coefficient of determination (R2) and relative percent deviation (RPD) values of 0.939 and 3.35, respectively, as well as the lowest root mean squared error (RMSE) value of 24.2 μg/g, indicating excellent model performance. This study provides valuable insights for the development and application of portable HSI equipment for rapid and non-destructive testing of Panax ginseng quality.

Thermal nondestructive testing: traditional approaches and novel trends (review)

In the last years, thermal testing has shown up as a quickly growing nondestructive evaluation technique. This is explained by wide implementation of composite materials in many industrial spheres, first of all, aviation and aerospace. Inspection of composites by means of traditional nondestructive testing (NDT) methods encounters some difficulties, which can successfully be overcome by using thermal NDT. Moreover, an overwhelming progress in development and commercialization of infrared imagers also took place in the last two decades. On the basis of earlier reviews in the field of thermal NDT, the author has attempted to summarize recent achievements in this field, including inspection methodologies and data processing, as well as development of thermal NDT equipment and applications of this technique. The review describes both national achievements and international approaches, which have been lately proposed in thermal NDT thus making this technique a very promising in NDT of some classes of materials and constructions.

Structural Optimization and Simulation of a Magnetic Leak Detection Device for Streak-like Defects in Oil Pipes

Striped defects are a common type of defects in oil pipelines, divided into horizontal and longitudinal stripes, and it is difficult to accurately quantify the results of conventional pipeline leakage magnetic detection to correspond to the type of defects and their extent. In this paper, a new type of oil pipe non-destructive testing equipment is proposed and designed based on the principle of magnetic leakage detection using three-dimensional finite element simulation, and the magnetic field distribution inside the equipment is simulated using ANSYS finite element analysis software to provide original data reference for the development of the new equipment.

Void Detection inside Duct of Prestressed Concrete Bridges Based on Deep Support Vector Data Description

The tendon that is inserted into the duct is a crucial component of prestressed concrete (PSC) bridges and, when exposed to air, can quickly corrode, and cause structural collapse. It can interpret the signal measured by non-destructive testing (NDT) to determine the condition (normal or void) inside the duct. However, it requires the use of expensive NDT equipment such as ultrasonic waves or the hiring of experts. In this paper, we proposed an impact–echo (IE) method based on deep support vector data description (Deep SVDD) for economical void detection inside a duct. Because the pattern of IE changes for various reasons such as difference of specimen or bridge, supervised learning is not suitable. Deep SVDD is classified as normal and defective, which is a broad distribution as a hypersphere that encloses a multi-dimensional feature space for normal data represented by an autoencoder. Here, an autoencoder was developed based on the ELMo (embeddings from language model)-like structure to obtain an effective representation for IE. In the experiment, we evaluated the performance of the IE data measured in different specimens. Thus, our proposed model showed an accuracy of about 77.84% which is an improvement of up to about 47% compared to the supervised learning approach.

A Novel Intelligent Rebound Hammer System Based on Internet of Things

In order to improve the test efficiency of concrete strength and ensure measured data reliability, we present a novel intelligent rebound hammer system which is based on the Internet of Things (IoT) and speech recognition technology. The system uses a STM32F103C8T6 microcontroller as the Main Control Unit (MCU), and one BC26 module as the communication unit, combined with a LD3320 voice recognition module and TOF050H laser ranging sensor to achieve the function of phonetic transcription and laser ranging. Without the need for traditional multi-person collaboration and burdensome data transfer, the system can collect the data of rebound value and location information and send them to the remote cloud information management system automatically in real time. The test results show that the system has high measuring accuracy, good data transmission stability and convenient operation, which could provide guidance for other types of non-destructive testing equipment designs.

A Review of Effectiveness on Quality Management System for Calibration Activity (ISO 9001:2015, ISO/IEC 17025:2017 and Conventional Method)

Non-destructive test (NDT) is well-known testing and acts as an important and mandatory part of the quality system for the automotive, aerospace, and oil & gas industry. There are several available non-destructive tests (NDT) methods such as ultrasonic, magnetic, eddy current, and radiographic that intend to provide a measurement for the NDT inspector to interpret the reading of the equipment. The result of interpretation will determine the quality and defect detection. The calibration process of NDT equipment is a major element to make sure the equipment is precise and accurate. The calibration process will help to detect any failure of components in the NDT equipment. However, the current normal practice in the industry does not follow the best quality management system to perform calibration activities. Therefore, this paper (review) aims to extract the resulting traceability, competency of the calibrator, and calculation of measurement uncertainty. The 3 main procedures as reference are ISO 9001:2015, ISO/IEC 17025:2017, and the conventional method. All the 3 mentioned quality management systems will be compared, and the best quality management system will be identified to support the calibration activity.

A new methodology to assess the remaining service life of motorway pavements at the network level from traffic speed deflectometer measurements

ABSTRACT Road agencies are looking for efficient pavement management tools to maintain adequate service levels and preserve the asset value. The traffic speed deflectometer (TSD) allows to continuously assess the structural condition of the pavement while moving at traffic speeds, thus overcoming the typical drawbacks of traditional non-destructive testing equipment. However, managing the huge amount of data deriving from TSD surveys is challenging, and there is no established method to use those data for identifying the remaining service life (RSL) of the pavement network. This study aimed at developing a new practical and reliable methodology to assess the RSL of motorway pavements at the network level from TSD measurements. The methodology is based on the use of the surface curvature index SCI300 and includes the temperature adjustment, the definition of a representative SCI300 value for each pavement section and the estimation of the residual fatigue resistance (considering bottom-up cracking as the critical distress). The methodology was implemented using the deflection data deriving from a TSD survey carried out on about 400 km of motorway pavement. The obtained RSL predictions can be used to identify the structural ranking of different pavement sections and guide budget forecasting, according to the actual maintenance and rehabilitation priorities.

Development of an Estimation Method for Depth of Spalling Damage in Concrete Pavement by Ultrasonic Velocity Measurement

As the amount of aged pavement increases, functional damage, such as spalling, occurs frequently on Portland Cement Concrete pavement (PCC) in South Korea. However, the existing management method does not properly reflect the scope of deterioration of the pavement causing early damage. To overcome the problem of the existing repair method, this study evaluated the deterioration of functional damage on the surface of the slab as soundness through ultrasonic velocity measurement method among non-destructive testing (NDT) techniques and suggested a method to estimate the depth of deterioration. To develop a method for estimating the depth of the deterioration a slab, a preliminary investigation was conducted to check the range of ultrasonic velocity of concrete pavement in South Korea and to evaluate the variability of NDT equipment. Based on the ultrasonic velocity, the sound rating of concrete pavement was graded from 5 for “very good” to 0 for “very poor”, and the tendency of the ultrasonic velocity to increase according to the depth of the deteriorated areas was confirmed.

Mobile Wall-Climbing Robot for NDT inspection of vertical concrete structures

Concrete structures, such as bridges or viaducts, play an important role in global road infrastructure. These types of structures are relatively expensive to build and they are susceptible to outer external influences, which in time deteriorate and lead to the reduction of their structural resistance. To reduce this effect, regular inspection is needed, which is often done manually by using specialized equipment to reach certain parts of bridges and viaducts. This process is both expensive and dangerous for the inspectors to conduct. Within the research project ASAP (Autonomous System for Assessment and Prediction of Infrastructure Integrity) in order to overcome these challenges, we have developed a prototype of a wall-climbing robot (WCR) for nondestructive testing (NDT). In this paper, different iterations of the developed WCR prototypes are presented. In four consecutive prototype designs, we have evaluated and upgraded the adhesion and locomotion system. Finally, a fifth prototype that carries the NDT equipment is presented. The final version of the WCR is equipped with robust and flexible adhesion that enables the robot to adhere to different types of surfaces. We have also addressed the challenges of integrating NDT equipment into the robot. To successfully conduct an inspection, besides the WCR, a safety system, control, and power systems are needed, which are further presented and discussed.

Autonomous, Digital-Twin Free Path Planning and Deployment for Robotic NDT: Introducing LPAS: Locate, Plan, Approach, Scan Using Low Cost Vision Sensors

Robotised Non Destructive Testing (NDT) presents multifaceted advantages, saving time and reducing repetitive manual workloads for highly skilled Ultrasonic Testing (UT) operators. Due to the requisite accuracy and reliability of the field, robotic NDT has traditionally relied on digital twins for complex path planning procedures enabling precise deployment of NDT equipment. This paper presents a multi-scale and collision-free path planning and implementation methodology enabling rapid deployment of robotised NDT with commercially available sensors. Novel algorithms are developed to plan paths over noisy and incomplete point clouds from low-cost sensors without the need for surface primitives. Further novelty is introduced in online path corrections utilising laser and force feedback while applying a Conformable-Wedge probe UT sensor. Finally, a novel source of data beneficial to automated NDT is introduced by collecting frictional forces of the surface informing the operator of the surface preparation quality. The culmination of this work is a new path-planning free, single-shot automated process removing the need for complex operator-driven procedures with a known surface, visualising collected data for the operator as a three-dimensional C-scan model. The dynamic robotic control enables a move to the industry 4.0 model of adaptive online path planning. Experimental results indicate the flexible and streamlined pipeline for robotic deployment, and demonstrate intuitive data visualisation to aid highly skilled operators in a wide field of industries.

Influence of temperature on the dielectric properties of asphalt mixtures

ABSTRACT When using non-destructive testing equipment to evaluate the quality of asphalt pavement, the asphalt pavement mixtures will exhibit different dielectric properties under dissimilar temperature conditions since the external electric field is affected by ambient temperature, which interferes with the accuracy of non-destructive testing. To improve the accuracy of non-destructive testing and quantify the influence of temperature on the dielectric properties of asphalt mixtures, a dielectric model suitable for describing the relationship between the relative dielectric constants of asphalt mixtures and temperature is established. The relative dielectric constant and dielectric loss of the two types of asphalt mixtures are measured at different temperatures to verify the accuracy of the dielectric model, and the goodness of model fit verifies wide applicability of the model. This temperature-based asphalt mixture dielectric model has great significance for quantifying the influence of temperature on the dielectric properties. Furthermore, the model shows that the dielectric properties of different asphalts and aggregates have different temperature sensitivity, and the concept of conductance activation energy proposed in this research provides innovative approaches for the optimisation of asphalt mixtures and raw materials.

Research on rail crack detection technology based on magneto-optical imaging principle

The detection of rail top crack is of great significance in ensuring the safe operation of railway. Based on the principle of magnetic flux leakage detection and Faraday magneto-optical effect, a high-resolution and non-destructive magneto-optical imaging detection method is proposed in this paper. This paper analyses the principle of magneto-optical imaging, establishes a rail magnetic flux leakage detection experimental system based on magneto-optical effect, and detects the rail specimen with crack through experiment. The experimental results show that the change of crack width and depth will affect the magneto-optical imaging results, and the minimum crack size that can be detected by this experimental system is 0.4mm wide and 0.5mm deep. This paper realizes the detection of rail crack defects by using magneto-optical imaging method, and provides the basis for building an efficient, portable and high-resolution rail non-destructive testing equipment.

Research on dairy products detection based on machine learning algorithm

In this study, an electronic nose model composed of seven kinds of metal oxide semiconductor sensors was developed to distinguish the milk source (the dairy farm to which milk belongs), estimate the content of milk fat and protein in milk, to identify the authenticity and evaluate the quality of milk. The developed electronic nose is a low-cost and non-destructive testing equipment. (1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis , LDA) for dimensionality reduction analysis, and finally use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source (cow farm) Identify the model and evaluate and compare the classification effects. The experimental results prove that the classification effect of the SVM-LDA model based on the electronic nose odor characteristics is better than other single feature models, and the accuracy of the test set reaches 91.5%. The RF-LDA and SVM-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate. The method of estimating the model, the results show that the RF model has the best estimation performance( R2 =0.9399 for milk fat; R2=0.9301for milk protein). And it prove that the method proposed in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of dairy products.

Dielectric properties of asphalt pavement materials based on the temperature field

Nondestructive testing equipment evaluates the quality of asphalt pavements and identifies different distresses based on the difference in the relative permittivity of asphalt pavement materials at different positions of the asphalt pavement structural layers. Thus, the accurate determination of the relative permittivity of asphalt pavement materials is essential for the quality evaluation of asphalt pavements. However, the dynamic temperature field inside the asphalt pavement will affect the performance and dielectric properties of each layer mixture and therefore affect the detection accuracy of nondestructive testing equipment.In order to analyze the influence of the temperature field on dielectric properties of asphalt pavement materials, the relative permittivity of each structural layer of the actual core specimen was measured at different temperatures by using the Dielectric Constant Test Platform, and a temperature-based dielectric composite model was developed. Meantime, the temperature field simulation of the typical asphalt pavement structures and distresses were carried out based on COMSOL finite element simulation, and the heat radiation, heat conduction, and heat convection were theoretically analyzed. On this basis, the relative permittivity and the relative permittivity correction rate of each structural layer was calculated to quantify the influence of the temperature field on the dielectric properties of asphalt pavement materials.This study confirmed that a crucial correlation exists between the temperature and relative permittivity of asphalt pavement mixtures. The temperature field distribution of distresses, namely, top-down cracks, reflective cracks, and road cavity, were simulated, and then the measured dielectric permittivity of each asphalt pavement structural layer was corrected. These findings can provide a theoretical basis for the temperature correction of detection data of nondestructive testing equipment and lay the foundation for the accurate identification of typical distresses in asphalt pavement.