Search

In this article we present an approach to the quantitative evaluation of the 3D printed sample made of polyethylene terephthalate glycol (PETG) using the active infrared thermography (AIT) method with halogen lamps excitation. For this purpose, numerical and experimental studies were carried out. The numerical model solved with finite element method (FEM) was used first to create a database of signals and further to train neural networks. The networks were trained to detect the heterogeneity of the internal structure of the tested printed sample and to estimate the defects position. After training, the performance of the network was validated with the data obtained in the experiment carried out with the active thermography regime on a real 3D print identical to the modelled one.

This paper presents a new approach to the extraction and analysis of information contained in magnetic Barkhausen noise (MBN) for evaluation of grain oriented (GO) electrical steels. The proposed methodology for MBN analysis is based on the combination of the Short-Time Fourier Transform for the observation of the instantaneous dynamics of the phenomenon and deep convolutional neural networks (DCNN) for the extraction of hidden information and building the knowledge. The use of DCNN makes it possible to find even complex and convoluted rules of the Barkhausen phenomenon course, difficult to determine based solely on the selected features of MBN signals. During the tests, several samples made of conventional and high permeability GO steels were tested at different angles between the rolling and transverse directions. The influences of the angular resolution and the proposed additional prediction update algorithm on the DCNN accuracy were investigated, obtaining the highest gain for the angle of 3.6°, for which the overall accuracy exceeded 80%. The obtained results indicate that the proposed new solution combining time–frequency analysis and DCNN for the quantification of information from MBN having stochastic nature may be a very effective tool in the characterization of the magnetic materials.

In this paper, we propose a new method based on active infrared thermography (IRT) applied to assess the state of 3D-printed structures. The technique utilized here—active IRT—assumes the use of an external energy source to heat the tested material and to create a temperature difference between undamaged and defective areas, and this temperature difference is possible to observe with a thermal imaging camera. In the case of materials with a low value of thermal conductivity, such as the acrylonitrile butadiene styrene (ABS) plastic printout tested in the presented work, the obtained temperature differences are hardly measurable. Hence, the proposed novel IRT method is complemented by a dedicated algorithm for signal analysis and a multi-label classifier based on a deep convolutional neural network (DCNN). For the initial testing of the presented methodology, a 3D printout made in the shape of a cuboid was prepared. One type of defect was tested—surface breaking holes of various depths and diameters that were produced artificially by inclusion in the printout. As a result of examining the sample via the IRT method, a sequence of thermograms was obtained, which enabled the examination of the temporal representation of temperature variation over the examined region of the material. First, the obtained signals were analysed using a new algorithm to enhance the contrast between the background and the defect areas in the 3D print. In the second step, the DCNN was utilised to identify the chosen defect parameters. The experimental results show the high effectiveness of the proposed hybrid signal analysis method to visualise the inner structure of the sample and to determine the defect and size, including the depth and diameter.

Stresses and deformations are some of the main factors influencing the mechanical and magnetic properties of steels. Resonance methods, based on the utilization of high-frequency electromagnetic fields, are the ones that can provide information about the course of the magnetization process. Moreover, according to skin effect, these methods may show sensitivity to surface deformations of the examined materials as well. As a rule, however, they are used to study the properties of materials of very limited sizes. This paper presents an approach in which a system based on the ferromagnetic resonance method FMR was considered for monitoring changes of characteristics related to magnetization dynamics of steel elements subjected to deformations. First, a solution was proposed, and then a numerical analysis, as well as a construction of the system, were presented. During the study, the possibility of carrying out measurements in a wide range of electromagnetic field conditions, enabling local inspection on structures, was also analysed. The system operation was verified using a set of samples made of low carbon steel film, representing distinct states of deformation. The obtained results make it possible to clearly distinguish changes in magnetic conditions, pointing to changes in the resultant magnetic anisotropy caused by the straining process.

The paper presents a new approach to non-destructive evaluation of easy/hard magnetization axis in grain-oriented SiFe electrical steels based on the Barkhausen phenomenon and its time-frequency (TF) characteristics. Anisotropy in steels is influenced by a number of factors that formulate the global relationship and affect the Barkhausen effect. Due to the observed high variability in the dynamics of magnetic Barkhausen noise (MBN) over time, obtained for various directions in grain-oriented steel, it becomes justified to conduct MBN signal analyses in the time-frequency domain. This representation allows not only global information from MBN signal over entire period to be expressed, but also detailed relationships between properties in time and in frequency to be observed as well. This creates the opportunity to supplement the information obtained. The main aspect considered in the work is to present a procedure that allows an assessment of the resultant angular characteristics in steel. For this purpose, a sample of a conventional grain-oriented SiFe sheet was used. Measurements were made for several angular settings towards the rolling and transverse directions. A data transformation procedure based on short-time Fourier transform (STFT) as well as quantitative analysis and synthesis of information contained in the TF space was presented. Angular characteristics of selected TF parameters were shown and discussed. In addition, an analysis of the repeatability of information obtained using the proposed procedure under various measurement conditions was carried out. The relationship between the selection of calculation parameters used during transformation and the repeatability of the obtained TF distributions were demonstrated. Then the selection of the final values ​​of the calculation parameters was commented upon. Finally, the conclusions of the work carried out were discussed.

In this paper, a transducer allowing nondestructive evaluation of material state in various directions is presented. The axial-symmetry transducer is using a matrix of vector magnetic field sensors. Each sensor is utilized for the observation of three orthogonal components. First, the construction is shown and the general performance is evaluated. Then, the data processing and integration algorithm for reconstruction of the signals is presented. Finally, the operation of the transducer is verified using a low-carbon steel plate having notches of different depths and aligned at various angles to the scanning axis. The results are presented and discussed.

The properties of magnetic Barkhausen noise (MBN) used for non-destructive testing of ferromagnetic materials are related not only to the magnetic properties of the material being tested. The effectiveness of the method also depends, to a large extent, on a number of factors defining the measuring conditions, including a frequency, a current and a shape of a field magnetizing an element under test. Therefore, the use of conditions enabling the highest possible discrimination level between different states of the tested magnetic materials becomes crucial. Measurement parameters have a direct impact on the quality of MBN features and, as a consequence, have a major impact on the final performance of the system. Due to the stochastic nature of the Barkhausen noise signal and complex characterization of its changes, this work analyzes the impact of measuring conditions based on its time-frequency representation. The results were given and discussed.

In this paper the results of utilization of electromagnetic methods operating in low and high frequency range for evaluation of stress state and plastic deformation in steel elements are presented. In low frequency range Barkhausen noise and magnetic hysteresis loop method for evaluation of stress level and growth of plastic deformation changes were utilized. The methods allow to monitor parameters related to magnetization process under AC filed. Additionally in this paper the possibility of utilization of high frequency method for estimation of deformation extent (i.e. elongation) caused by stress will be presented. In this experiment the frequency response (the reflection coefficient S11) is measured. The strong relation of antennas resonant frequency to patch dimensions is utilized in order to obtain information about deformation of the sample.

This paper presents in detail the experimental methodologies and the results of the infrared investigation of steel samples under load. Moreover, the second electromagnetic method (EM) for nondestructive evaluation was used to monitor the fatigue process. The infrared thermography allows one to observe the global condition of the material, while the EM carries out local, detailed examination of the fatigue stage in the critical region. The presented inspection results are compared and discussed.

Two nondestructive methods are used for monitoring of fatigue process in steel samples under cyclic loading: the infrared thermography (IRT) and electromagnetic (EM). The IRT allows to observe the general global condition of the material while the EM to carry out local, detail examination of the fatigue stage in critical region. Both methods have different physical basics what allows to obtain a wider range of information about the state of the structure. Streszczenie. W pracy wykorzystano dwie nieniszczące metody badan do monitorowania procesu zmeczeniowego stalowych probek poddanych obciązeniom cyklicznym: metode termografii podczerwonej (IRT)j i elektromagnetyczną (EM). Metoda IRT umozliwia obserwacje globalnego stanu badanego materialu natomiast metoda EM pozwala na dokladną lokalną inspekcje stanu zmeczeniowego obszarow krytycznych. Z uwagi na fakt wykorzystywania odmiennych wlaściwości fizycznych do pośredniej obserwacji stanu struktury przez obie metody mozliwe jest uzyskanie szerszego zakresu komplementarnych informacji o zmianach zmeczeniowych. Dwie nieniszczące metody badan do monitorowania procesu zmeczeniowego stalowych probek poddanych obciązeniom cyklicznym

The inspection of thin basalt fiber reinforced composite materials was carried out using two nondestructive methods: terahertz time domain imaging and infrared thermography. In order to combine the information about the defects arising in examined materials the inspection results were parametrized. In order to acquire more information content, new approximation based features are proposed. Then, a knowledge extraction based multivariate analysis of preselected features’ vector was carried out. Finally, in order to integrate features distributions of representing different dynamic level of information, a multiresolution wavelet based data fusion algorithm was applied. The results are presented and discussed.

The utilization of a glass–adhesive–steel joint in modern machine and vehicle production is constantly growing. Despite the technological regime during the production process, various defects in the adhesive joints may occur. One of the major problems is lack of adhesive between joined materials. Due to the widespread use of non-transparent layers increasing adhesion, it is frequently not possible to conduct simple visual inspections. Hence, it is important to develop a non-destructive adhesive path continuity examination procedure. In that process, the different physical properties of the joint materials must be taken into account. Therefore, in this paper various testing methods were used, including ultrasound, thermographic and electromagnetic methods operating in the microwave and terahertz frequency range. Different physical phenomena of the methods allowed for examination of the joint in a wide context. In order to verify the information brought by each method, the results were transferred into common coordinate space, compared and finally integrated. Various opinion pooling strategies were conducted to fuse data, which allowed us to emphasize convergent and complementary information on adhesive continuity. The obtained results are promising and constitute the basis for further work on an integrated system for automatic evaluation of a wide range of possible defects in glass–adhesive–steel joints.

The paper presents a new approach to obtain information on magnetic anisotropy in Si&ndash, Fe grain oriented ferromagnetic steel based on the observation of the magnetic Barkhausen noise (MBN). Until now, in the literature one can only notice the MBN study of magnetic anisotropy in steels carried out in a single time or frequency domain. However, due to the observed high variability of the dynamics of the MBN phenomenon over its occurrence period, depending on the steel properties, the idea of utilization of combined time and frequency representations to obtain new or supplementary information arises. For this purpose, the MBN phenomenon was observed in various directions for steels with oriented magnetic properties. Then, using the short-time Fourier transform, time-frequency (TF) distributions were determined and features vectors enabling the quantification of crucial information were determined. Before performing the final experiments, a series of tests were carried out for different measuring conditions. As a result, it was possible to adjust the conditions enabling us to obtain the highest possible sensitivity for MBN and discrimination level between directional properties in the material. Then, an algorithm of detailed analysis and division of the TF representation into subranges was proposed, enabling the extraction of more detailed information about the phenomena occurring during the magnetization process. This allowed us to clearly indicate and then separate three areas of MBN main activity. Finally, the obtained angular distributions of selected features were presented and discussed, and further conclusions were given.

The application range of Barkhausen noise (BN) technique to nondestructive examination of steel condition or properties is constantly growing. However, the stochastic nature of Barkhausen's noise forces utilization of advanced data processing techniques to extract the knowledge allowing quantitative description of observed signals. Many factors can affect various properties of BN signals derived from time and frequency domain. Therefore, both modes should be considered during feature extraction process. In this paper, in order to combine the information provided in time and frequency regimes, a detailed analysis of time-frequency representation of Barkhausen noise signals allowing quantitative evaluation was carried out. The processing procedures were presented and utilized for estimation of damage progress of stress loaded steel samples. The results were shown and discussed.

The properties of the Magnetic Barkhausen Noise (MBN) utilized for non-destructive examination of ferromagnetic materials are strongly affected by the measuring conditions. Therefore many aspects must be analysed prior adjusting of the testing such as frequency, magnetisation current value, waveform shape, configuration of excitation coil and detection coil, air gap between the magnetisation unit and testing sample, MBN frequency range or sampling frequency range. These all parameters are closely linked to MBN features quality and have strong influence on the final efficiency of the system. Therefore in this paper an evaluation of influence of measurement conditions on systems performance was carried out. The results were given and discussed.

In this paper a machine learning algorithm was applied to analyze the integrated data responses of magnetic multi-sensor matrix transducer. In order to proceed the algorithm, a large database was prepared considering measurement as well as simulation results. A finite element method was utilized to conduct a numerical calculation and provide transducers response signals for the various configurations of defects. The deep artificial neural network was used for implementation of machine learning algorithm and then the model was verified.

Different phases which exist in a duplex stainless steel (DSS) affect mechanical as well as electromagnetic properties. Therefore, an eddy current (EC) technique can be utilized to detect phases inclusions. In order to evaluate and identify the amount of deleterious phases it is important to assess the impact of electromagnetic properties of material on a measuring signal. This paper presents a study on the influence of DSS properties changes caused by a sigma (σ) phase on signal measured by an EC transducer. The results of an analytical and numerical analysis are presented. The analytical modeling allows to identify the electromagnetic properties and evaluate the amount of σ phase in the tested material.

In this paper the technique of defects evaluation in composite materials based on a combination of active thermography method and terahertz technique is presented. The composite glass fiber sample with delaminations produced by load conditions was chosen for the tests. Active Infrared thermography with halogen lamps and terahertz technique are sensitive to changes in different physical parameters of the tested material. The combination of these techniques, performed by the data fusion of obtained measurements results allows to obtain the full information on the detected material's defect.

An eddy current method was applied to evaluate amount of sigma phase in duplex stainless steel. In the preliminary experiments several transducers configuration were considered. The performance of the transducers were verified utilizing samples having different amount of sigma phase. The configurations presenting the greatest possibility of evaluation of sigma phase were chosen for further experiments. In this paper a dedicated transducer operating in differential mode will be presented. The results will be shown and discussed.

An effective method for inspection of billets made of titanium alloy is proposed. Two nondestructive testing (NDT) techniques were utilized: phase array ultrasonic testing (PA) and eddy current testing (ECT). In this paper, results obtained by ECT method will be shown and discussed. A dedicated transducer was introduced and optimized. The results of numerical simulations as well as measurements will be shown and discussed.

Increasing the number of inspection sources creates an opportunity to combine information in order to properly set the operation of the entire system, not only in terms of such factors as reliability, confidence, or accuracy, but inspection time as well. In this paper, a magnetic sensor-array-based nondestructive system was applied to inspect defects inside circular-shaped steel elements. The experiments were carried out for various sensor network strategies, followed by the fusion of multisensor data for each case. In order to combine the measurements, first data registration and then four algorithms based on spatial and transformed representations of sensor signals were applied. In the case of spatial representation, the data were combined using an algorithm operating directly on input signals, allowing pooling of information. To build the transformed representation, a multiresolution analysis based on the Laplacian pyramid was used. Finally, the quality of the obtained results was assessed. The details of algorithms are given and the results are presented and discussed. It is shown that the application of data fusion rules for magnetic multisensor inspection systems can result in the growth of reliability of proper identification and classification of defects in steel elements depending on the utilized configuration of the sensor network.

In this paper, stress- and fatigue-loaded ferromagnetic steel samples were evaluated using three different remanent flux leakage transducers. Giant magnetoresistive sensors were used as the pickup elements. Each transducer has its own integrated local magnetizing section, which allows us to magnetize the tested material in different directions. The defect detection dependency on angle, at which it occurs to the magnetizing field and sensing element, is minimized by gathering and then integrating the information from transducers. The performance of the transducers was verified using a steel plate (S355J2G3) with electro-discharge machined notches. Further experiments were carried out using fatigue- and stress-loaded steel samples.

Magnetic Barkhausen Noise (MBN) is an effective non-destructive testing approach for ferromagnetic material residual stress measurement and evaluation. However, the common MBN signal features such as the time to peak value and frequency spectrum are not accurate enough for stress evaluation due to the randomness of MBN signal. This paper proposed a new method to calculate a curve which looks like magnetic hysteresis loop (B-H curve) based on the line integral from envelope curve of MBN signal. And some new features are extracted from the above integrated B-H curve. These new signal features show a good linear relationship with applied stress. By comparing these new features with previous common features, we find that the envelope area and center height difference of new features can effectively reduce the adverse impact on stress evaluation due to the randomness of MBN signal.

This paper presents in detail the experimental methodologies and the results of the infrared investigation of the steel samples under load. Moreover the second, electromagnetic method (EM) for non destructing evaluation was used for monitoring the fatigue process. The IRT allows to observe the general global condition of the material while the EM to carry out local, detail examination of the fatigue stage in critical region. The presented inspection results are compared and discussed. 1. Introduction The dynamic development of many modern industries is associated with the production of the steel structures with a high level of complexity. It is expected that these structures meet the highest standards of safety. However, maintaining relatively low manufacturing costs are also crucial. The success of many companies and corporation is mainly based on the possibility of the long term and low cost product usage. Therefore the continuous and systematic monitoring of the changes in the material properties of the steel structures is very important. The crucial feature of such a monitoring is the necessity of observation the structure under load. The estimation of fatigue failure risk requires to carry out the analysis and prediction algorithms taking into consideration initial strength capacity, production technology and types of possible flaws introduced during production or operation period [1]. The process requires also determination of local condition of the material, which involves evaluation of its microstructure, applied and residual stress values in accordance to strain state as well as crack grow area within the examined region. Therefore, the need for application of nondestructive testing and evaluation methods contributing the process of determining the state of stress and damage in the material emerges [1, 2]. There are many methods, that can be useful in steel structures monitoring (electromagnetic, magnetic, radiographic and ultrasound). In the recent times the increased importance of thermographic method may be also observed [3]. Active infrared thermography is one of the methods that are widely used in the non-destructive testing of materials. Internal structure of the tested material, in particular defects (such as cracks or weakening of the structure caused by the mechanical stress) affect the free flow of heat. Sensitive infrared camera is used to observe the temperature distribution on the sample's surface. The high temperature resolution allows to observe even small internal inhomogeneities in the form of the overheated or colder places, showing the location, size and type of defects. The purpose of this paper is to present and compare the results of fatigue monitoring carried out by electromagnetic and infrared thermography method [4]. 2. Experimental methods and systems

The structural integrity of wheelsets used in rolling stock is of great importance to the safety. In this paper, electromagnetic system with an eddy current transducer suitable for the inspection of hollow axles have been presented. The transducer was developed to detect surface braking defects having depth not smaller than 0.5 mm. Ultrasound technique can be utilized to inspect the whole axle, but it is not sufficiently sensitive to shallow defects located close to the surface. Therefore, the electromagnetic technique is proposed to detect surface breaking cracks that cannot be detected by ultrasonic technique.

The purpose of this paper is to present the results of observation of fatigue failures forming under cyclic dynamic loading using selected electromagnetic NDT methods. To evaluate fatigue damage a Barkhausen noise observation, an AC magnetization, a flux leakage and eddy current method were used.

The magnetic leakage flux, multifrequency eddy current and Barkhausen noise methods were used to evaluate changes in materials properties induced by stress. Seven samples made of ferromagnetic low carbon steel with different stress level were prepared. The results showed tensile deformed samples at room temperature with a yield at around 350 MPa, residual strain between 0.7% and 14% and tensile stress from 100 MPa to 389 MPa.

The paper presents and shortly discusses the AI algorithms created for automated detection and classification of weld defects on the basis of radiography images. The three approaches are described: with use of fuzzy logic, with use of artificial neural networks and that basing on rough sets theory. The data evaluating the accuracies of obtained classifiers as well as chosen examples are attached.

The structural integrity of train axles is one of the important factors for safety precautions in a railway industry. Most common techniques used nowadays in the railway industry for manual inspection are visual and magnetic particle techniques as well as ultrasound testing. In this paper, a novel system for electromagnetic inspection is proposed to meet the aforementioned requirements. The system consists of two subsystems and transducers dedicated for flux leakage and eddy current method. Results of measurements and evaluation of the transducers are provided.

Permanent magnets are widely used in many applications. A fast and reliable system for online evaluation of permanent magnets is required for their quality assurance. Therefore, a new measurement system has been developed to measure the spatial distribution of the vector magnetic field. The system consists of a complex Hall transducer, an analog multiplexer, a mechanical XYZ scanner and a control computer. The matrix of Hall sensors is designed in this way that all 3 components can be measured at once in multiple points. Such kind of transducer enables to reduce the time needed for inspection. The similar matrix transducer was also utilized to evaluate state of magnetized ferromagnetic materials.

The paper presents a software implementation of an Intelligent System for Radiogram Analysis (ISAR). The system has to support radiologists in welds quality inspection. The image processing part of software with a graphical user interface and a welds classification part are described with selected classification results. Classification was based on a few algorithms: an artificial neural network, a k-means clustering, a simplified k-means and a rough sets theory.

In this paper an application, implemented functions and data base of welds defects destined to an Intelligent System for Radiogram Analysis ISAR for welds quality inspection is presented. System has to improve information contained in the radiogram, extract defects and decide on welds quality taking into account the relevant regulations.