In this study, an artificial neural network application was performed to tell if 18 plates of the same material in different shapes and sizes were cracked or not. The cracks in the cracked plates were of different depth and sizes and were non-identical deformations. This ANN model was developed to detect whether the plates under test are cracked or not, when four plates have been selected randomly from among a total of 18 ones. The ANN model used in the study is a model uniquely tailored for this study, but it can be applied to all systems by changing the weight values and without changing the architecture of the model. The developed model was tested using experimental data conducted with 18 plates and the results obtained mainly correspond to this particular case. But the algorithm can be easily generalized for an arbitrary number of items.

In this study, it was achieved by using the method of impulse noise to detect internal or surface cracks that can occur in the production of ceramic plates. Ceramic materials are often used in the industry, especially as kitchenware and in areas such as the construction sector. Many different methods are used in the quality assurance processes of ceramic materials. In this study, the impact noise method was examined. This method is a test technique that was not used in applications. The method is presented as an examination technique based on whether there is a deformation on the material according to the sound coming from it as a result of a plastic bit hammer impact on the ceramic material. The application of the study was performed on plates made of ceramic materials. Here, it was made with the same type of model plates manufactured from the same material. The noise that would occur as a result of the impact applied on a point determined on the materials to be tested has been examined by the method of time-frequency analysis. The method applied gives pretty good results for distinguishing ceramic plates in good condition from those which are cracked.

Due to the wide range of various sheet metal grades and the need to verify the material properties, there are numerous methods to determine the material formability. One of them, that allows quick determination of sheet metal formability, is the Erichsen cupping test described in the ISO 20482: 2003 standard. In the presented work, the results of formability assessment for DC04 deep drawing sheet metal were obtained by means of the traditionally carried out Erichsen cupping test and compared with the results obtained by using two advanced methods based on vision analysis. Application of these methods allows extending the traditional scope of analysis during Erichsen cupping test by determination of the necking and strain localization before fracture. The proposed methods were compared in order to dedicate appropriate solution for the industrial application and laboratory tests respectively, where the simplicity and reliability are the mean aspects need to be considered when applied to the Erichsen cupping test.

Active thermography is an efficient tool for defect detection and characterization as it does not change the properties of tested materials. The detection and characterization process involves heating a sample and then analysing the thermal response. In this paper, a long heating pulse was used on samples with a low thermal diffusivity and artificially created holes of various depths. As a result of the experiments, heating and cooling curves were obtained. These curves, which describe local characteristics of the material, are recognized using a classification tree and divided into categories depending on the material thickness (hole depths). Two advantages of the proposed use of classification trees are: an in-built mechanism for feature selection and a strong reduction in the dimensions of the pattern. Based on the experimental study, it can be concluded that classification trees are a useful tool for the thinning detection of homogeneous material.

Advanced vision method of analysis of the Erichsen cupping test based on laser speckle is presented in this work. This method proved to be useful for expanding the range of information on material formability for two commonly used grades of steel sheets: DC04 and DC01. The authors present a complex methodology and experimental procedure that allows not only to determine the standard Erichsen index but also to follow the material deformation stages immediately preceding the occurrence of the crack. Accurate determination of these characteristics in the sheet metal forming would be an important application, especially for automotive industry. However, the sheet metal forming is a very complex manufacturing process and its success depends on many factors. Therefore, attention is focused in this study on better understanding of the Erichsen index in combination with the material deformation history.

Nondestructive and contactless online approaches for detecting defects in polymer films are of significant interest in manufacturing. This paper develops vision-based quality metrics for detecting the defects of width consistency, film edge straightness, and specks in a polymeric film production process. The three metrics are calculated from an online low-cost grayscale camera positioned over the moving film before the final collection roller and can be implemented in real-time to monitor the film manufacturing for process and quality control. The objective metrics are calibrated to correlate with an expert ranking of test samples, and results show that they can be used to detect defects and measure the quality of polymer films with satisfactory accuracy.

Wind turbines are among the key equipment needed for eco-friendly generation of electricity. Maintaining wind turbines in excellent technical condition is extremely important not only for safety but also for efficient operation. Studies indicate that defects in the external structure of a turbine blade reduce energy production efficiency. This research investigated the potential of the terrestrial laser scanning technology to examine the technical conditions of wind turbine blades. The main aim of the study was to examine whether terrestrial laser scanning measurements can be valuable for wind turbine blade condition surveying. The investigation was based on the radiometric analyses of point clouds, which forms the novelty of the present study. Condition monitoring focuses on the detection of defects, such as cracks, cavities, or signs of erosion. Moreover, this study consisted of two stages. The next objective entailed the development and examination of two different measurement methods. It was then identified which method is more advantageous by analysing their effectiveness and other economic considerations.

Infrared (IR) reflectography has been used for many years for the detection of underdrawings on panel paintings. Advances in the fields of IR sensors and optics have impelled the wide spread use of IR reflectography by several recognized Art Museums and specialized laboratories around the World. The transparency or opacity of a painting is the result of a complex combination of the optical properties of the painting pigments and the underdrawing material, as well as the type of illumination source and the sensor characteristics. For this reason, recent researches have been directed towards the study of multispectral approaches that could provide simultaneous and complementary information of an artwork. The present work relies on non−simultaneous multispectral inspection using a set of detectors covering from the ultraviolet to the terahertz spectra. It is observed that underdrawings contrast increases with wavelength up to 1700 nm and, then, gradually decreases. In addition, it is shown that IR thermography, i.e., temperature maps or thermograms, could be used simultaneously as an alternative technique for the detection of underdrawings besides the detection of subsurface defects.