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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.

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.

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.

Due to the existing relationship between microstructural properties and magnetic ones of the ferromagnetic materials, the application potential of the magnetic Barkhausen noise (BN) method to non-destructive testing is constantly growing. However, the stochastic nature of the Barkhausen effect requires the use of advanced signal processing methods. Recently, the need to apply time-frequency (TF) transformations to the processing of BN signals arose. However, various TF methods have been used in the majority of cases for qualitative signal conditioning and no extensive analysis of TF-based information has been conducted so far. Therefore, in this paper, the wide analysis of BN TF representation was carried out. Considering the properties of TF transformations, the Short-Time Fourier Transform (STFT) was used. A procedure for definition of the envelopes of the TF characteristic was proposed. To verify the quality of extracted features, an analysis was performed on the basis of BN signals acquired during stress loading experiments of steel elements. First, the preliminary experiments were processed for various parameters of the measuring system and calculation procedures. The feature extraction procedure was performed for different modes of TF representations. Finally, the distributions of TF features over the loading stages are presented and their information content was validated using commonly used features derived from time T and frequency F domains.

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 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.

Eddy current transducer with sensing coils placed orthogonally and connected in differential mode was introduced to evaluate fatigue cracks in clad pipeline circumferential welds. A dedicated embedded electronic hardware was developed to drive the transducer and measure the electrical complex impedance of the coils, and was specifically designed for operation under autonomous in-line inspection tool. In the laboratory experiments, an automated inspection was performed with the goal to evaluate transducer&rsquo, s detectability, and different scanning speed was tested to reproduce in service situation. The results have confirmed that the introduced eddy current transducer is a potential solution for fatigue crack detection in clad circumferential weld root, while the hardware developed presented a reasonable SNR reaching the data rate required to be incorporated in an autonomous in-line inspection tool.

Nowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor matrix transducer for characterization of defected areas in steel elements and a deep learning based algorithm for integration of data and final identification of the object state. The transducer allows sensing of a magnetic vector in a single location in different directions. Thus, it enables detecting and characterizing any material changes that affect magnetic properties regardless of their orientation in reference to the scanning direction. To assess the general application capability of the system, steel elements with rectangular-shaped artificial defects were used. First, a database was constructed considering numerical and measurements results. A finite element method was used to run a simulation process and provide transducer signal patterns for different defect arrangements. Next, the algorithm integrating responses of the transducer collected in a single position was applied, and a convolutional neural network was used for implementation of the material state evaluation model. Then, validation of the obtained model was carried out. In this paper, the procedure for updating the evaluated local state, referring to the neighboring area results, is presented. Finally, the results and future perspective are discussed.

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.

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.

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.