Vol 55, No 4 (2019)

We present results of acoustic studies of hydrogenated samples of PT-7M and PT-3V titanium alloys, aimed at establishing correlations between acoustic parameters and the mass fraction of hydrogen in the samples. Relevant metallographic studies have been carried out, which make it possible to describe the nature of structural changes occurring under hydrogen saturation. The transition to an embrittled state in the alloys under study has been demonstrated by microhardness measurements.

Nowadays, composite patches are widely used to repair damaged metal structures in different industries. In order to assure the perfect performance of these structures, the bonding between composite patches and metal structures should be inspected. Ultrasonic Lamb waves are among the common methods for assessment of the bonding between the composite patches and metal plates. The main goal of this research is to evaluate the sensitivity of the ultrasonic Lamb wave modes in bonding inspection. For this purpose, dispersion curves of an aluminum plate bonded to a composite patch were obtained and using the through thickness stress distribution, the sensitivity of existing modes to the bonding was evaluated. An experimental examination was also performed to generate the possible modes obtained from the theory and to show the effect of disbond on the output signals. Based on the experimental results, the mode which had been predicted to be more sensitive to the bonding was more affected by the disbond. Three Lamb wave modes were excited for inspecting the disbond between aluminum and composite with the angle wedge transducer. As it was theoretically predicted, the Lamb wave mode with higher values of shear stress in the adhesive layer was more affected by the disbond. On the other hand, disbond detection using the mode with poor values of shear stress at the adhesive layer was difficult. These results were concluded from the amplitude change percentage due to disbond in the experiments.

Evaluating the specific electrical conductivity of two-layered nonmagnetic objects by the pulsed eddy-current method is considered. Eddy-current transducer signals are investigated using a finite element model. Based on simulation results, depending on the ratio of the parameters of the coating and the base of a test object, two methods are proposed for determining the specific electrical conductivity by integration of magnetic flux. The working capacity of the proposed technique has been confirmed in a laboratory experiment.

The article presents results of the investigation of the quantitative evaluation of the degree of damage, described by the measure of accumulated plastic strain obtained in a static tensile test, using selected non-destructive techniques. Inconel 718 alloy was tested. The tests were conducted using a new type of specimens of variable cross-sectional area of measuring part. This provided a continuous distribution of plastic strain in the gage part of the specimen. The permanent deformation that varies along the sample axis enables an analysis of damage induced by a plastic deformation. The proposed method enables replacing the series of specimens by one sample. Degradation of the alloy corresponds with the changes of the electromagnetic properties of the material—the phase angle of the complex impedance of the eddy current, as well as with acoustics properties of material—acoustic birefringence of ultrasonic waves. It allows to determine the degree of damage of the material using noninvasive, non-destructive methods. Using the damage parameter proposed by Johnson it is possible to obtain the correlation between the non-destructive results and a damage degree of the material. The presented testing method delivers information about changes in the material structure caused by permanent deformation.

Results of developing and testing a betatron-based tomograph with an energy of up to 10 MeV are presented. The tomograph allows investigating objects with a shined-though thickness of up to 250 mm in steel equivalent. In contrast to foreign analogs, a horizontal product arrangement is used; it creates the most favorable conditions for revealing such defects as cracks and delaminations, since in this position, the product is subjected to small deformations due to gravitational forces. The proposed scanning scheme has the properties of a third-generation tomograph and preserves the quality of the tomogram and the time it is gathered with a reduced number of detectors and tomograph dimensions. The results of evaluating the tomograph sensitivity with a test sample and the results of testing products containing defects are provided.

This manuscript is concerned with the development of digital inspection of casting defects using x-ray radiography images. An efficient approach for detection and classification time of casting defects in x-ray radiography images is proposed. The accuracy of this approach depends on suggested algorithms for background correction, image de-noising, image enhancement and image segmentation of casting defects. Three different algorithms are introduced for automatic detection of casting defects in x-ray images. These algorithms depend on features extraction power density spectrum (PDS) and high order statistics (HOS). An artificial neural network is utilized as a classifier for matching purposes of extracted features. The results show that HOS achieved the best recognition rate of 100% for casting defects in X-ray radiography images in comparison with other algorithms. Besides, a reduction of classification time for casting defects is another target in this paper. It is achieved using costly powerful digital processing hardware and advanced software. Furthermore, an algorithm is realized to reduce classification time of casting defects. This algorithm depends on textural features that extracted from x-ray images of casting defects. Hence, a feature reduction program code is implemented for reduction of extracted features. This program code is relied on average value of each extracted feature for normal and defect image. The numbers of extracted features are reduced from 22 to 2 features. Therefore, better execution time can be achieved for classification purposes of casting defects. The proposed algorithms are evaluated using Intel core TM i5-3470 CPU with 3.20 GHz and Intel core TM i7-3612QM CPU with 4.00 GHz. Consequently, these algorithms can be transferred into more powerful digital processing hardware such as FPGA and GPU for faster classification of casting defects. The obtained results confirm that proposed algorithms can be applied for a broad range of non-destructive applications using image processing techniques.