https://www.jndttech.ir/ NDT Technology en daily 1 Wed, 19 Feb 2025 00:00:00 +0330 Wed, 19 Feb 2025 00:00:00 +0330 https://www.jndttech.ir/article_222075.html In this paper, a novel method for non-destructive evaluation of the type and severity of damage in HP40Nb steam reformer tubes is presented based on the eddy current method. Beyond simple defect detection, this method provides numerical representation of various types of microstructural damage. Field and laboratory inspections were performed on tubes from three reformer units of an Iranian petrochemical industrial unit with nominal operating lives of 5, 13, and 16 years. Eddy current inspection was performed by the point scanning method with an electromagnetic sensor with a T-shaped core at a frequency of 60 kHz using a handheld portable device. Microstructural studies of samples of these tubes were performed with optical and scanning electron microscopes equipped with EDS analysis. The results of the microstructural laboratory studies showed that the main damages in the damaged pipes include changes in the amount and type of carbides, reduction in the chromium content of the matrix, carburization, oxidation, and microcracking. Eddy current evaluations showed that the microstructural and the eddy current variations can be characterized by appropriate parameters. Intrinsic damage (DI) due to carburization and oxidation can be evaluated using the phase angle index and creep damage (DII), which is manifested by the formation of holes and cracks and can be evaluated using the impedance index. It has been observed that by increasing the amount of carburization and the conversion of M23C6 carbides to M7C3 and reducing the chromium content of the matrix, the phase angle index increases from 18.7 to a maximum of 63.2 degrees. Also, as the pipes' life increases and the creep cracks' nucleation and growth occur, the impedance index increases from a dimensionless value of 655 to 1064. Finally, by combining the two electromagnetic indices and presenting an empirical relationship, the total failure rate index (Dtotal) of each pipe is presented. https://www.jndttech.ir/article_222076.html This study thoroughly examines the detection of cracks and incomplete fusion in butt-welded steel plates. These defects, widely recognized as critical welding flaws, can significantly undermine the structural integrity and long-term reliability of metallic systems. To investigate this pressing issue, controlled and artificial defects, specifically cracks and incomplete fusion, were introduced during the experimental phase. These defects were systematically generated by inducing abrupt changes in the welding temperature and voltage during the welding process. Traditional approaches in this domain predominantly rely on ultrasonic waves and high-frequency sensors, which often entail high computational complexity and significant financial costs. In contrast, this study proposes an innovative method that utilizes the characteristics of elastic waves propagating through steel plates, presenting a more straightforward, accessible, and cost-efficient alternative. The proposed method incorporates data acquisition and analysis within a limited frequency range, which mitigates environmental influences and reduces computational demands, thereby facilitating the continuous monitoring of welded joints. The defect identification process involves analyzing the signal energy and higher-order statistical parameters, such as the third- and fourth-order statistical moments, to effectively differentiate between cracks and incomplete fusion. Experimental results indicated that in samples containing cracks, the ratios of signal energy, third-order statistical moments, and fourth-order statistical moments to those of defect-free samples increased by 125%, 394%, and 53%, respectively. Conversely, in samples with incomplete fusion, these ratios were found to be 76%, 28%, and 86%, respectively. These findings clearly underscore the efficiency, robustness, and accuracy of the proposed method for non-destructive testing of welding defects. The approach offers reduced computational complexity, making it a viable tool for practical industrial applications. Furthermore, the simplicity of this method and its capability for real-time monitoring highlight its great potential for integration into the continuous health monitoring of metallic structures. This approach offers a promising and practical alternative to conventional, more resource-intensive techniques for welded joint evaluation. https://www.jndttech.ir/article_222078.html X - ray - based backscattering imaging has attracted increasing attention worldwide and has found a variety of applications in the industry , including non -destructive testing, security inspection , and quality control . In the present study, a backscattering imaging device with a pencil X- ray beam was designed and manufactured . The scanner includes an industrial X - ray generator, two plastic detectors, a chopper wheel, a data collection system, a data processing software, and MATLAB - based image forming . The rotary chopper wheel creates pencil X - rays that sweep the entire sample . The performance of the system was evaluated through a series of tests using oil and paint cans ( full and semi - filled ) . The findings confirm that backscattering imaging is very effective for low atomic and low -density objects such as adhesives, paints, and oils and provides high - quality images . Also , combining the data of both detectors and applying a low - pass filter to the images will significantly improve the quality of the backscatter images . The inherent advantage of backscattering imaging devices is the scanning of materials with low atomic numbers ( adhesives , paints, fluids derived from petroleum, plastics, polymers , explosives, and even aluminum ) , which the results of this study attest to . The findings of this study showed that a wide range of industrial applications can be drawn for this device . The next activity of this paper will be to use the spatial division method to process the signals and compare it with the time division algorithm ( the present study ) . Also , imaging of other light materials is also on the authors' agenda . . . . . . . . . .. . . . https://www.jndttech.ir/article_222080.html ABSTRACTRadiography with gamma and neutron rays are two important methods for examining objects that cannot be observed with the naked eye. These include internal anatomy, airplane parts, and nuclear reactor fuels. Neutron and gamma radiography are both non-destructive testing techniques used to inspect the internal structure of objects, but they have key differences. Neutron radiography uses thermal neutrons to penetrate materials, interacting with atomic nuclei rather than electrons. This method is beneficial for detecting light elements like hydrogen and materials with high atomic numbers, making it valuable in aerospace, nuclear, and materials science fields for identifying defects in components. In contrast, gamma radiography uses high-energy photons known as gamma rays that interact with both atomic nuclei and electrons. This technique is effective for inspecting thicker and denser materials and is commonly used in industrial radiography for inspecting welds, detecting corrosion, and examining castings.This research aims to determine whether combining images can provide a better understanding of the object. Radiography images of 11 samples from 11 different materials were taken: Uranium-238 (238U), Natural Uranium (U Nat), Uranium-235 (235U), Lead (82Pb), Silver (47Ag), Copper (29Cu), Nickel (28Ni), Tin (50Sn), Carbon (C 6), Polyethylene (C2H4), and Water (H2O). The study utilized a high-energy gamma source (Cobalt-60Co), a low-energy gamma source (Iridium-192Ir), and a thermal neutron source (energy 0.025 eV). The research was conducted using the MCNP code, and the output data were converted into images using MATLAB software.The results indicate that combining neutron and gamma radiography images provides more information about the object under radiography. This combination enhances the clarity and contrast of the images, enabling more precise identification of the internal structures and features of the material. By integrating these two methods, more comprehensive information about the material's properties can be obtained, allowing for more accurate analysis and aiding in the detection of defects and internal characteristics. https://www.jndttech.ir/article_222082.html Nowadays the use of radiography to inspect weld defects is of great importance in different industries. Given the various causes for image quality reduction in radiography systems, the use of image processing to enhance the contrast of radiographs is crucial. Artificial Intelligence (AI), as one of the most advanced technologies of the modern era, plays a significant role in the image processing, where machine learning and deep learning algorithms are employed to analyze and interpret visual data. In this research, Facebook AI Similarity Search (FAISS) has been used to improve the contrast of weld radiographs. FAISS is a powerful and optimized library for similarity search in large datasets, developed by Facebook. The results of processing the radiographs show that the contrast has increased in various regions, particularly in the weld root and defect areas, where gas porosity and lack of fusion are most prevalent, showing a significant improvement. These results have been evaluated by radiography experts, who confirm that, in addition to improving the contrast of radiographs in different regions, the defect detection can be carried out efficiently. In addition, this method is fast and does not require complex manual adjustments. One of the key advantages of this method is the use of a pre-trained network, which saves time and costs associated with training new models. This is particularly important in large industries such as oil and gas, where time and accuracy of detection are critical. Given the positive results of this research, it is expected that the use of AI and libraries like FAISS will become a standard tool in the processing and analysis of radiography images in the future, bringing about a fundamental transformation in the quality and speed of defect detection, and it can help to identify the weld defects and discontinuities by radiography and welding specialists. https://www.jndttech.ir/article_222084.html Selecting the most suitable Non-Destructive Testing (NDT) method in industries such as energy, transportation, automotive, aerospace, and oil and gas significantly contributes to quality improvement, minimizing human errors, and reducing operational costs. In this study, a smart, data-driven classification model is developed using Machine Learning (ML) techniques to recommend the most appropriate Non-Destructive Testing method for weld inspection, based on technical parameters such as weld type, thickness, base material, structural complexity, and accessibility to the weld area. A dataset containing 500 real-world Non-Destructive Testing records, including Ultrasonic Testing (UT), Eddy Current Testing (ET), Magnetic Particle Testing (MT), Radiographic Testing (RT), and Liquid Penetrant Testing (PT), was collected and preprocessed through steps such as normalization, encoding of categorical features, and missing value handling. To evaluate the model, four widely used classification algorithms—Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost)—were implemented in Python. Model performance was assessed using 10-fold cross-validation to ensure robustness and generalization, and hyperparameters were tuned via a grid search strategy for optimal configuration. The results demonstrated that the XGBoost model outperformed the other classifiers, achieving 90% overall accuracy along with superior precision, recall, and F1-score values. Moreover, the model exceeded the average performance of human experts by approximately six percent in predicting the correct Non-Destructive Testing method for samples with different material types, geometries, and weld configurations. The model’s predictions were consistent with international standards such as ASME Section V (2023) and ASTM E-Series, confirming its technical reliability and compliance. The proposed approach integrates both data-driven insights and the predictive power of classification algorithms, making it an effective and practical decision-support tool for selecting Non-Destructive Testing methods in welding applications. It enhances inspection planning, reduces variability caused by human judgment, and ensures more consistent and reliable assessments across different inspection scenarios. https://www.jndttech.ir/article_222086.html One of the efficient, cost-effective, and rapid methods for non-destructive quality evaluation of horticultural and agricultural products is the acoustic analysis response generated by impact. In this research, the possibility of using sound processing resulting from the collision of hazelnuts with a galvanized iron plate for separating large, medium, and small hazelnuts, as well as separating nutty and hollow types, has been investigated. First, hazelnuts were divided into four groups based on size: large, medium, small, and empty. The hazelnuts of each group were weighed to separate the nutty and hollow types from the composition. Based on this approach, a system was designed and developed using a single-impact method to generate sound from hazelnuts upon impact with an iron surface. Artificial intelligence techniques were employed to analyze and classify the product based on features extracted from their acoustic signals. Then, in order to take samples, all hazelnuts were dropped from a height of 35 and 45 cm and hit an iron plate. The sound of the collision was recorded by the microphone. The recorded data is taken from the time domain to the frequency domain by Fast Fourier Transform (FFT) and graded using the features obtained from the gradation. The results of audio tests show that by comparing the direct ratio of weight with amplitude and sound pressure level, a hazelnut that has a pass range of less than 0.121Pa, and a sound pressure level of less than 75.2 dB is hollow or has an immature kernels. The study also revealed that the drop height did not significantly affect the quality classification, and a height of 35 cm, along with a sound pressure level above 75.2 dB, yielded the best results for hazelnuts classification. The results of this research, in combination with the common methods of separating hazelnuts, show that acoustic response analysis as a non-destructive method of grading hazelnuts can reduce their damage in addition to increasing the speed and accuracy of calssification. https://www.jndttech.ir/article_222087.html The objective of this study was to examine the in-plane shear properties of filament-wound composite cylinders and assess the associated damage mechanisms using acoustic emission techniques. Two distinct composite materials, namely carbon/epoxy and glass epoxy, were manufactured for this purpose. The cylinders were subjected to torsion load conditions in accordance with ASTM D5448. Throughout the testing process, various parameters such as torque, angle of torsion, and strain were meticulously recorded. Additionally, the acoustic emission sensors were utilized to capture signals indicating the occurrence of damages. The findings of the study indicate that the in-plane shear strength and shear modulus of the carbon/epoxy specimens surpass those of the glass/epoxy counterparts. The results from acoustic emission testing indicated that in the CFRP specimens, there were no instances of fiber/matrix debonding. Instead, the main mode of damage observed was fiber breakage, accounting for approximately 71% of the total damage detected. On the other hand, in the GFRP specimens, the primary damage mechanism was found to be fiber/matrix debonding, making up approximately 50% of the overall damage recorded.The objective of this study was to examine the in-plane shear properties of filament-wound composite cylinders and assess the associated damage mechanisms using acoustic emission techniques. Two distinct composite materials, namely carbon/epoxy and glass epoxy, were manufactured for this purpose. The cylinders were subjected to torsion load conditions in accordance with ASTM D5448. Throughout the testing process, various parameters such as torque, angle of torsion, and strain were meticulously recorded. Additionally, the acoustic emission sensors were utilized to capture signals indicating the occurrence of damages. The findings of the study indicate that the in-plane shear strength and shear modulus of the carbon/epoxy specimens surpass those of the glass/epoxy counterparts. The results from acoustic emission testing indicated that in the CFRP specimens, there were no instances of fiber/matrix debonding. Instead, the main mode of damage observed was fiber breakage, accounting for approximately 71% of the total damage detected. On the other hand, in the GFRP specimens, the primary damage mechanism was found to be fiber/matrix debonding, making up approximately 50% of the overall damage recorded.