Search

Adhesive bonds, including car putty coatings, are used in the construction of modern motor vehicles. Therefore, it is important to improve methods that allow nondestructive evaluation of the properties of these joints. The main objective of this study was to evaluate selected properties of putty coatings such as the width of the applied coating and adhesion to the substrate based on changes in ultrasonic wave parameters. The research was carried out in two stages. In the first, the values of the surface wave amplitude were determined as a function of the width of the coating to the substrate. It was found that as the width of the coating increases, the amplitude of the surface wave pulse decreases. The second stage involved correlation studies to relate the reflection coefficient |r| to the adhesion of the coating to the substrate. Based on the results, it was found that as the value of the reflection coefficient decreases, the value of the coating’s adhesion to the substrate increases. The determined values of this parameter range from 0.30 to 0.86, which correspond to the adhesion of the range 1.51 to 18.97 MPa. The obtained test results have practical significance and can be used in evaluating the condition of coatings in vehicle body repair shops.

Structural health monitoring (SHM) is focused on the systematic monitoring of the state of structures to determine their safety, dependability, and durability [...]

In this paper, a combination of Magnetic Barkhausen Noise (MBN) and several classical machine learning (ML) methods were used to evaluate both the grade and the magnetic directions of conventional and high grain oriented electrical sheets subjected to selected surface engineering methods. The presented analysis was conducted to compare the performance of two machine learning approaches, classical ML and deep learning (DL), in reference to the same MBN examination problem and based on the same database. Thus, during the experiment, 26 classical ML algorithms were used including decision trees, discriminant analysis, support vector machines, naïve Bayes, nearest neighbor, artificial neural networks and ensemble classifiers. The experiments were carried out considering a different number of recognized magnetic directions and hence the number of determined classes as well. The results of classification accuracy of the applied ML methods were compared with those obtained for the DL model presented in a previous paper. The highest accuracy was obtained for ML models based on artificial neural networks and ensemble bagged trees. However, the accuracy did not reach 89% in the best case—for the smallest number of determined classes. Nevertheless, the achieved results generally indicated an approx. 10 percent advantage of the deep learning model over the classical ones in terms of accuracy in each of the considered cases.

Magnetic Barkhausen Noise (MBN) is a method being currently considered by many research and development centers, as it provides knowledge about the properties and current state of the examined material. Due to the practical aspects, magnetic anisotropy evaluation is one of such key areas. However, due to the non-stationary and stochastic nature of MBN, it requires searching for postprocessing procedures, allowing the extraction of crucial information on factors influencing the phenomenon. Advances in the field of the analysis of non-stationary signals by various transformations or decompositions resulting into new time- and frequency-related representations, allow the interpretation of complex sets of signals. Therefore, in this paper, several time-frequency transformations were used to analyze the data of MBN for the purpose of the magnetic anisotropy evaluation of electrical steel. The three main transform types with their modifications were considered and compared: the Short-Time Fourier Transform, the Continuous Wavelet Transform and the Smoothed Pseudo Wigner–Ville Transform. By using Exploratory Data Analysis methods and the parametrization of time-frequency representation, the qualitative and quantitative analysis was made. The STFT presented good performance on providing useful information on MBN changes while simultaneously leading to the lowest computational efforts.