The eddy-current and ultrasonic investigations of the nickel-base superalloy the gas turbine engine blades after exploitation

The work carried out microstructural, eddy-current and ultrasonic studies of the material of spent blades of gas turbine engines made of nickel-base superalloy. To determine the degree of damage to the material of the spent blades, studies of the microstructure were carried out on a scanning electron microscope. It was found that γ' coagulation occurred in some specimens, which corresponds to overheated material. Acoustic studies of the material were carried out using the ultrasonic pulse echo method. Comparative analysis showed that in such specimens there is a significant decrease in the velocity of propagation and attenuation of longitudinal ultrasonic waves. It is found that the readings of the eddy-current flaw detector in overheated specimens differ more than twice in comparison with non-overheated specimens. This fact can be used for operational non-destructive testing of the actual state of the metal structure by the eddy current method with the aim of further safe operation of gas turbine engines.

Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method

It's well-known that industrial safety is now a top concern. Nowadays, accidents caused by flammable gases occur frequently in our everyday lives. Gas cylinders, which are used for household purposes, wide range of businesses, and vehicles are often reported to be on the verge of exploding. Explosions have left a large number of individuals seriously wounded or could also be lethal in certain cases. This project's goal is to use a HOG features for SVM classifier which is used to identify pipeline gas leaks and keep tabs on them. In addition, the system utilises an image processing technique to identify pipeline fractures. Early detection and identification of pipeline flaws is a predominant aspect of this study. According to the suggested design, the robot capture the image down the pipe, looking for any signs of gas leakage by the Eddy Current method. This type of recognition has proved superior to other traditional methods. The methods with efficiency parameters and the results were compared and are tabulated in the results section. In the future, the data in the course of detection could be sent through GSM to a mobile application.

Eddy current tomography for visualization of cracks in aircraft riveted joints

The control of the subsurface areas of metal products is necessary in many technological processes. So, for example, in aerospace technology it is essential to determine the presence of defects in aircraft engines. The same problems arise in chemical, power and other industries, which are letting out the highly technological equipment one of the widely known methods for inspecting metal products in the aviation industry is the eddy current method. This method is widely used to control small microscopic defects inside conductive materials. This method allows to ensure the safety of the operation of various products and devices in many areas of modern industry. An eddy current detector (probe) is a device, which induces eddy currents into metal objects and then detects the magnetic fields produced by these eddy currents. A magnetic field is created by a coil, or set of coils, through which a time-varying electrical current is driven. The frequency regime is sufficiently low, a few hertz to a few hundred kilohertz, so the targets of interest are within the near field of the transmitter. Considering the high conductivity of study samples, we can define that used waves in metals are located in the millimeter wave band. An eddy current imaging can be considered near-field imaging and a device allowing obtaining the eddy current images as a scanning near-field microscope. The obtained experimental results showed that the proposed tomographic method is effective for studying various complex inhomogeneities under the metal surface

Effect of Temperature on Electrochemically Assisted Deposition and Bioactivity of CaP Coatings on CpTi Grade 4

Calcium phosphate (CaP) coatings are able to improve the osseointegration process due to their chemical composition similar to that of bone tissues. Among the methods of producing CaP coatings, the electrochemically assisted deposition (ECAD) is particularly important due to high repeatability and the possibility of deposition at room temperature and neutral pH, which allows for the co-deposition of inorganic and organic components. In this work, the ECAD of CaP coatings from an acetate bath with a Ca:P ratio of 1.67, was developed. The effect of the ECAD conditions on CaP coatings deposited on commercially pure titanium grade 4 (CpTi G4) subjected to sandblasting and autoclaving was presented. The physicochemical characteristics of the ECAD-derived coatings was carried out using SEM, EDS, FTIR, 2D roughness profiles, and amplitude sensitive eddy current method. It was showed that amorphous calcium phosphate (ACP) coatings can be obtained at a potential −1.5 to −10 V for 10 to 60 min at 20 to 70 °C. The thickness and surface roughness of the ACP coatings were an increasing function of potential, time, and temperature. The obtained ACP coatings are a precursor in the process of apatite formation in a simulated body fluid. The optimal ACP coating for use in dentistry was deposited at a potential of −3 V for 30 min at 20 °C.

Rail Surface Defect Detection and Analysis Using Multi-Channel Eddy Current Method Based Algorithm for Defect Evaluation

The railroad rail support trains and contributes to their operation. Internal and surface defects occur on the rail due to various combinations of causes including fatigue loading and cyclic tension and compression among others from the deterioration of the rail along with the temperature differences of seasonal changes. Surface defects such as head check, shelling, and squats start out in the rail head and become internal defects due to poor maintenance, ultimately resulting in rail failure. In order to prevent rail failure, it is important that defects are identified through nondestructive evaluation (NDE) in advance and to carry out maintenance techniques including grinding. NDE methods include MFL, EMAT, and ECT, and among these, the ECT method is a representative method with excellent detection sensitivity that nondestructively inspects metal surfaces such as rails and pipes using an electromagnetic field. Also, since the defect signal is obtained as an electrical signal, the depth, length, and width of defects can be assessed using a defect evaluation algorithm. This study investigated the field applicability and future practical use of the 16 channel eddy current testing equipment and defect evaluation algorithm developed in this study. Therefore, the field applicability of the equipment and defect evaluation algorithm was investigated through the detection of artificial defects with varying size and depth. Afterwards, future practical use was evaluated by inspection of areas of rail that are in use and with naturally occurring surface defects and analysis of their size (length, width), depth, and phenomena.

Extended Damage Detection and Identification in Aircraft Structure Based on Multifrequency Eddy Current Method and Mutual Image Similarity Assessment

In this paper, a novel approach to Non-Destructive Testing (NDT) of defective materials for the aircraft industry is proposed, which utilizes an approach based on multifrequency and spectrogram eddy current method combined with an image analysis method previously applied for general-purpose full-reference image quality assessment (FR IQA). The proposed defect identification method is based on the use of the modified SSIM4 image quality metric. The developed method was thoroughly tested for various locations, sizes, and configurations of defects in the examined structure. Its application makes it possible to not only determine the presence of cracks but also estimate their size.

Evaluation of Loose Assemblies Using a Multi-frequency Eddy Current Method and Artificial Neural Networks

The paper deals with the non-destructive evaluation of the airgap existing between parts in loose metallic assemblies, using the eddy current (EC) method. In this study, the relationship between the variations of the impedance of a ferrite-cored coil sensor and an assembly featuring two aluminum plates is analyzed. Then artificial neural networks, based on statistical learning of the relationship between a sensor and an assembly are proposed and developed using both simulated and measured multi-frequency EC data, so as to estimate the distance between the assembly parts in a range from 0 µm to 500 µm. For the neural network built on experiment data, the inaccuracy of obtained results is smaller than 1.06%.