نشریه فناوری آزمون های غیرمخرب
سال سوم شماره 4 (پیاپی 14، پاییز و زمستان 1402)

Todays, in the oil and gas industry, storage tanks are used to store oil and petroleum products. Industry experience indicates that soil-side corrosion would occur at sever rates at most above ground storage tanks bottom. Since these damages happen at the bottom surface of the tanks and are not visible with visual inspection, a trustable method is needed that can inspect the storage tanks periodically. According to the researches have carried out until now, the most common technology to identify defects in the bottom surface of the storage tanks is the magnetic flux leakage inspection technique (MFL), which can perform the inspection as quickly as possible with proper accuracy. For this purpose, in this article, the aim is to build a magnetic flux leakage inspection device that can perform inspection and identification. There are many various parameters affect the MFL inspection such as environmental conditions, operational parameters and etc. Among the operational parameters the effective variables which have important roles in inspection are: the distance of the sensors to the center of the defect point, the distance between the magnet poles and the distance between the magnets and the tank bottom plate. Also, the reference plates used in this project are steel plates with 6 mm and 10 mm thicknesses include the artificial defects created on the surface according to ASME Section V Article 16. In order to reduce the costs and time of experimental tests Ansys Maxwell simulation program was used and the results obtained from the simulation were used to conduct experimental tests. The results of simulation are compared with the results of experiment. The results showed that by changing the mentioned effective parameters, defects with the depth of 20% to 80% can be detected with trustable accuracy. Also, by changing the parameters in some conditions, the lower depths of 20% can be detectable.

Honeycomb sandwich panels are lightweight yet strong structures. These panels have features such as thermal insulation due to their honeycomb structure. The honeycomb sandwich panel structure is a combination of high flexural strength and flexural strength with low weight. These materials are widely used in the aviation industry. Manufacturing defects and operational damage have emerged as an important safety concern, and as a result, the need for non-destructive testing to identify defects and damage during operation and maintenance has increased. In addition to detecting defects, it is very important to accurately identify or classify them. In this article, the insulating cryogenic composite tank wall has been inspected by a non-destructive method. Four sandwich samples with a honeycomb structure were designed and manufactured using two CFRP plates and a Nomex core. On one side of the samples, there was 5 mm of resin with nano clay 30B, which was mixed by ultrasonic mixing method. This structure was designed as thermal insulation and gas barrier. Infrared thermography was used to detect and categorize defects in the honeycomb structure. These defects included adhesive separation, delamination, presence of air in the carbon shells and resin layer, and fracture in the honeycomb core. Thermography results showed that by using thermography, defects in the outer shell and resin can be identified with high accuracy, but the accuracy is lower in the inner shell and core.According to the thermography results, it was observed that all defects were detected up to the fourth layer, but in the lower layers (layers 5, 6 and 7) defects such as Teflon, Kapton and dry carbon layers could not be detected. In addition, based on observations, defects such as lack of adhesion from the side of the resin layer due to high thickness and insufficient heat transfer could not be identified. Using the temperature-location diagram, the size of the defects up to 1 mm depth was determined with high accuracy.

X-ray imaging of electronic boards is widely used in reverse engineering to identify circuits, how they are connected, and their damage. In this method, by passing the X-ray through single or multi-layer boards, the way they are connected and how the parts are arranged are determined. Investigations show that due to the scattering of X-rays and the thinness of the copper connections on the boards and the small dimensions of the electronic components, there is fogging in the radiographs. In this research, to increase the contrast, the modified complete change method (MTV) with alternating gradient, which is an iterative method based on gradient changes in complete changes, has been used. The results show that with the implementation of the MTV algorithm on the radiographs of different ranges, the contrast has increased and besides the copper connections have become clearer, the internal components of the electronic components have also become clearer. According to experts, about 20 to 40% contrast improvement can be seen in reconstructed images. This information can be used to repair or build boards in reverse engineering.Keywords: Industrial Radiography, Reverse Engineering, Electronic Board, Modified Total Variation method, Image Processing.Abstract: X-ray imaging of electronic boards is widely used in reverse engineering to identify circuits, how they are connected, and their damage. In this method, by passing the X-ray through single or multi-layer boards, the way they are connected and how the parts are arranged are determined. Investigations show that due to the scattering of X-rays and the thinness of the copper connections on the boards and the small dimensions of the electronic components, there is fogging in the radiographs. In this research, to increase the contrast, the modified complete change method (MTV) with alternating gradient, which is an iterative method based on gradient changes in complete changes, has been used. The results show that with the implementation of the MTV algorithm on the radiographs of different ranges, the contrast has increased and besides the copper connections have become clearer, the internal components of the electronic components have also become clearer. According to experts, about 20 to 40% contrast improvement can be seen in reconstructed images. This information can be used to repair or build boards in reverse engineering.Keywords: Industrial Radiography, Reverse Engineering, Electronic Board, Modified Total Variation method, Image Processing.

Measuring defect sizes is very important in determining weld strength. This size is also used to evaluate the object on the test for accept or reject criteria according to the relative standards. One of the important methods for measuring different defects' sizes is industrial radiography testing (RT), a non-destructive testing method. Radiography is carried out using penetrating X or Gamma rays. Radiography is a volumetric method and can give information from the inside the different objects. Various factors, such as the magnification of the radiographic image, the fogginess of the radiographs, the non-point source, and the inherent scattering of X-rays, affect on the measurement and the accuracy of the defect sizing. In particular, size measurement for defects with larger distance respect to the film or radiography detector can be with more uncertainty. This is due to more shadowiness of rays with larger distances. For radiography imaging, an industrial Computed Radiography System (CR) was used. General purpose imaging phosphor plates with a laser resolution of 50 micrometers have been used. The X-ray source was an industrial powerful X-ray tube with a voltage of up to 300 kilovolts. In this research, utilizing the distance measurement between the lines in the duplex image quality indicator (DIQI) tool as a known length, the size of defects in standard parts in Sonakit educational test kit that have specific defects is estimated and compared with the original value. To decrease the blurring, a recursive filter method is used to make the edges sharper to better estimate the defect sizes. The results show that the measurement of defects is related to the accuracy of the user in estimating the pixels of the defect regions and the sharpness of the edges. The measurement error is reported between 6% and 19% for the defect measurement in the standard parts examined.

This research addresses the detection of camshaft axial clearance defects in internal combustion engines during end-of-line hot testing using acoustic signal processing. The study's importance lies in reducing manufacturing costs and enhancing customer satisfaction through improved product quality. This defect is prevalent in the production line and challenging to identify using conventional methods.The research proposes an intelligent solution for defect detection by combining time-frequency domain signal processing techniques with artificial neural networks. The axial clearance defect was simulated at various severity levels, and acoustic signals were recorded using a handy sound recording device at three different engine speeds (1300, 1700, and 2500 RPM) under no-load conditions. The engines undergo a seven-minute test at these different speeds to ensure proper functionality. The choice of a handy recorder device was based on the manufacturer's request for a portable and cost-effective solution.It's noteworthy that the recorded audio data contains noise due to production line conditions, adding complexity to the fault diagnosis process. For signal processing and feature extraction, two methods were employed: Continuous Wavelet Transform (CWT) and Mel Spectrogram.The results demonstrate that the Mel Spectrogram is more effective for feature extraction compared to the Continuous Wavelet Transform. At operating speeds of 1700 RPM and 2500 RPM, all defect levels are detected with an average accuracy of 99% by using Convolutional Neural Network.This study contributes to the field of non-invasive fault detection in automotive manufacturing, offering a reliable and cost-effective method for identifying camshaft axial clearance defects. The high accuracy achieved at specific operating speeds suggests that this approach could be implemented in real-world production environments, potentially leading to significant improvements in quality control processes and overall product reliability.The success of this method highlights the potential of acoustic signal processing and machine learning techniques in solving complex industrial quality control challenges.

Non-destructive tests are the most important and efficient tests for checking welded objects. Non-destructive tests are used to identify defects in the internal and external parts of welded objects, that focus on identifying defects of the parts without damaging them. In this article, the industrial radiography method, which is one of the non-destructive methods, is used to detect surface and subsurface welding defects such as cracks, holes, corrosion, etc. In the radiography method due to the inherent scattering of X-rays, noises from the X-ray machine, attenuation of the beam in the examined object, geometrical factors such as the size of the radiation source, the thickness of the part and the distance between the source and the film, etc., the resulting images may lack clarity. Despite these factors, the quality of radiographs is low, and it is difficult to accurately interpret the results and identify the welding defects. Therefore, Image processing methods are used as an auxiliary tool to increase contrast and improve the interpretation of radiographs. In this article, the aim is to increase the quality of welding radiographs using the iterative weighted median filter, which is a part of spatial domain filters. The background image of the original radiograph is obtained by applying the iterative weighted median filter with a same number of repetitions and the different 3D windows. Then, the background image obtained is subtracted from the original radiograph, reconstructed images are obtained with high contrast. The results show that the application of this filter on the 3D radiographs of the weld better reveals the defects hidden in the radiographs of the weld, including small and subsurface cracks. Also, the expert's evaluation shows that the reconstructed images have a contrast improvement between 5 and 16% in terms of contrast, and they define the defect regions with better and more accurate resolution.

A substantial volume of oil and gas pipelines, particularly in petrochemical industries, are insulated with various materials. In many cases, a small opening or leak can allow water and other corrosive liquids to penetrate, leading to corrosion under insulation (CUI). This type of corrosion is not detectable through conventional methods over the insulation. With the usual methods of non-destructive inspection, such as visual and ultrasonic tests, it is not possible due to the lack of access to the metal of the pipeline. One of the most effective non-destructive methods for this purpose is the use of radiographic profiling devices. In this paper, using the Monte Carlo method, the common X-ray spectra for this technique at 70 keV and 90 keV were simulated. Industrial radioisotopes used, including Ir-192 and Co-60, were considered as sources. The pipeline phantoms implemented were made of 6mm thick carbon steel and 24mm thick polyethylene insulation. Artificial corrosion with a depth of 3mm was created on the pipeline wall. A single-energy linear detector made of CsI(Tl) with a thickness of 5mm and a pixel pitch of 1.6mm was implemented in the code, and the spatial signal was recorded on it. The transport of X-ray and gamma photons for the used sources was visually recorded for 20,000 photons, and the signal recorded on the detector, along with the accumulated energy in each detector pixel, was measured. The results indicate the suitable performance of the implemented system in locating and detecting CUI defects.A substantial volume of oil and gas pipelines, particularly in petrochemical industries, are insulated with various materials. In many cases, a small opening or leak can allow water and other corrosive liquids to penetrate, leading to corrosion under insulation (CUI). This type of corrosion is not detectable through conventional methods over the insulation. With the usual methods of non-destructive inspection, such as visual and ultrasonic tests, it is not possible due to the lack of access to the metal of the pipeline. One of the most effective non-destructive methods for this purpose is the use of radiographic profiling devices. In this paper, using the Monte Carlo method, the common X-ray spectra for this technique at 70 keV and 90 keV were simulated. Industrial radioisotopes used, including Ir-192 and Co-60, were considered as sources. The pipeline phantoms implemented were made of 6mm thick carbon steel and 24mm thick polyethylene insulation. Artificial corrosion with a depth of 3mm was created on the pipeline wall. A single-energy linear detector made of CsI(Tl) with a thickness of 5mm and a pixel pitch of 1.6mm was implemented in the code, and the spatial signal was recorded on it. The transport of X-ray and gamma photons for the used sources was visually recorded for 20,000 photons, and the signal recorded on the detector, along with the accumulated energy in each detector pixel, was measured. The results indicate the suitable performance of the implemented system in locating and detecting CUI defects.

Blades play a vital role in the performance of wind turbine systems. However, they are prone to damage from complex and irregular loading, which can lead to catastrophic degradation and costly maintenance. Defects or damage to wind turbine blades (WTBs) not only reduce the lifetime and efficiency of wind turbine electricity generation but also increase monitoring errors, safety hazards, and maintenance costs. Therefore, damage detection for WTBs is of high importance for preventing failures, planning maintenance, and ensuring the operational stability of wind turbines. This review article provides a comprehensive overview of advanced damage detection techniques for WTBs, including many updated methods based on strain measurement, acoustic emission, ultrasound, thermography, and machine vision. With the development of machine vision approaches and image processing technology, machine vision-based detection has become a promising and primary technique for detecting surface damage and measuring the deformation of WTBs [28] due to its low cost, ease of operation, and no need for prior knowledge about damage locations. In this review article, common damages to WTBs are introduced comprehensively at first. In the second stage, the principles of detection, development methods, advantages, and disadvantages of the aforementioned techniques for blade inspection are examined. The trend in damage detection in wind turbines is moving towards methods that offer full capabilities, long-range, contactless, non-destructive, wireless, and online monitoring. Timely detection of blade damage and continuous assessment of the structural health of wind turbines are increasingly important. This review article proposes a new idea for the design and integration of energy harvesters and WTB damage detection methods, in which the devices are automatic and wireless. Finally, potential research directions, and WTB damage detection techniques through comparative analysis, are reviewed and concluded. This review is expected to provide guidelines for practical WTB inspections as well as research perspectives for damage detection techniques.

In this paper, we present an innovative, non-destructive method for the simultaneous evaluation of the elastic properties and attenuation coefficients of polymeric filaments utilized in the Fused Deposition Modeling (FDM) additive manufacturing process. This method is grounded in the theory of acoustic wave scattering, where polymer filaments are immersed in water and subjected to acoustic waves. The scattered waves carry detailed and comprehensive information about the elastic properties and attenuation coefficients of the filaments. To extract this information, we employ an inverse method that involves comparing the resonance frequencies observed in the scattered signals with the predictions made by a theoretical model. Our data analysis process integrates advanced techniques, including deconvolution and genetic algorithms, which enable the precise and accurate measurement of critical parameters such as longitudinal and transverse velocities, density, and both longitudinal and transverse attenuation coefficients in a single experiment. The findings from our experiments reveal that, for the ABS filaments studied, the longitudinal and transverse velocities are 2280 m/s and 956 m/s, respectively, while the longitudinal and transverse attenuation coefficients are measured at 0.012ka and 0.024ka Nepers, respectively, with a filament density of 1006 kg/m³. These experimental results were validated through comparison with ultrasonic pulse-echo tests performed on ABS rods with a diameter of 25 mm, confirming the accuracy of our method. Specifically, the errors in measuring longitudinal and transverse velocities were found to be less than 2.5% and 13%, respectively, while the density measurement exhibited an impressively low error margin of just 0.2%. Furthermore, the error in the longitudinal attenuation coefficient at a frequency of 0.5 MHz was approximately 6%, and the error in the transverse attenuation coefficient at a frequency of 5 MHz was about 12%. This research not only demonstrates the accuracy and reliability of our proposed method but also contributes significantly to a deeper understanding of the properties and behavior of polymers used in additive manufacturing processes

The application of composite components has significantly increased in the various industries, including oil and gas, aerospace, marine, and automotive sectors. Among these, carbon fiber reinforced polymers (CFRP) are particularly prevalent in the aerospace industry due to their superior properties like high strength to weigh ratio. Given the critical nature of these composites, non-destructive inspection is essential to ensure their integrity and performance. Ultrasonic testing is one of the most widely used methods for inspecting these materials. Recent research has focused on the application of ultrasonic testing for the non-destructive inspection of CFRP parts. This study investigates the feasibility of using through-transmission ultrasonic testing with a water jet technique for inspecting of the CFRP components. Additionally, it examines the relationship between flexural properties and the intensity of transmitted waves in multilayer composite materials. For this purpose, some samples were fabricated from epoxy-carbon fiber using the hand lay-up method. A through-transmission ultrasonic non-destructive testing setup with a water jet system was designed and constructed, and ultrasonic testing was performed to identify defects in these samples. In this set-up, a uniform water column is used to transmit the pressure wave from the transducer to the surface of the samples under test, which replaces the couplant material. Subsequent bending tests were conducted to determine the flexural properties of the samples. The analysis revealed that an increase in the number of layers led to a decrease in both the final flexural strength and the average intensity of the transmitted wave, attributed to the higher likelihood of structural defects in samples with more layers. Subsequent bending tests were conducted to determine the flexural properties of the samples. The analysis revealed that an increase in the number of layers led to a decrease in both the final flexural strength and the average intensity of the transmitted wave, attributed to the higher likelihood of structural defects in samples with more layers.