This paper proposes a new approach to defect detection system design focused on exact damaged areas demonstrated through visual data containing gear wheel images. The main advantage of the system is the capability to detect a wide range of patterns of defects occurring in datasets. The methodology is built on three processes that combine different approaches from unsupervised and supervised methods. The first step is a search for anomalies, which is performed by defining the correct areas on the controlled object by using the autoencoder approach. As a result, the differences between the original and autoencoder-generated images are obtained. These are divided into clusters using the clustering method (DBSCAN). Based on the clusters, the regions of interest are subsequently defined and classified using the pre-trained Xception network classifier. The main result is a system capable of focusing on exact defect areas using the sequence of unsupervised learning (autoencoder)–unsupervised learning (clustering)–supervised learning (classification) methods (U2S-CNN). The outcome with tested samples was 177 detected regions and 205 occurring damaged areas. There were 108 regions detected correctly, and 69 regions were labeled incorrectly. This paper describes a proof of concept for defect detection by highlighting exact defect areas. It can be thus an alternative to using detectors such as YOLO methods, reconstructors, autoencoders, transformers, etc.

Force measurement is a science discipline that experiences significant progress with the introduction of new materials and evaluation methods. Many different sensor types, working on different principles, have been developed and reviewed and have found use in medicine as well as many other industries. New trends and demands require a size reduction and simple applicability, with the use of, for example, micro electromechanical systems ($MEMS$). For purposes of this study, the initial $MEMS$ body is supplemented by its scaled version. Force measurement in this study works on the force to time-delay conversion principle. A compact compliant mechanical body ($CCMB$) with an embedded parallel resonant circuit ($PRC$) acting as a transducer realizes the conversion. Depending on the resonant frequency of the transducer ($CCMB$ or $MEMS$), we have measured the applied force based on the reverse influence of the transducer on the surrounding $EM$ field. The analysis shows that the transducer’s resonant frequency has a detectable reverse influence on the voltage-controlled oscillator ($VCO$) $DC$ supply current. The force influencing the transducer is determined by the $DC$ supply current ripple position during the $VCO$ frequency sweep. The study presents the method proposal and mathematical analysis, as well as its function verification by simulation and prototype measurements. The proposed principle was validated on a $CCMB$ prototype capable of measuring forces up to ∼$2.5$ N at a sampling frequency of ∼23 kHz, while the measured time-delay ranges from $14.5$ µs to $27.4$ µs.

This paper deals with the concept of the automated calibration design for inspection systems using laser sensors. The conceptual solution is based on using a laser sensor and its ability to scan 3D surfaces of inspected objects in order to create a representative point cloud. Problems of scanning are briefly discussed. The automated calibration procedure for solving problems of errors due to non-precise adjustment of the mechanical arrangement, possible tolerances in assembly, and their following elimination is proposed. The main goal is to develop a system able to measure and quantify the quality of produced objects in the environment of Industry 4.0. Laboratory measurements on the experimental stand, including the principal software solution for automated calibration of laser sensors suitable for gear wheel inspection systems are presented. There is described design of compensation eccentricity by Fourier transform and sinusoidal fitting to identify and suppress the first harmonic component in the data with high precision measuring.

The direction for zero-emission transport is largely driven by the adoption of battery-powered vehicles. A critical aspect of a successful battery storage system is the production of high-quality electrodes, which necessitates rigorous inspection processes and defect detection systems. In this...

In this paper we present the capacitance study of the intrinsic amorphous silicon/crystalline silicon heterostructure with the aim to gain insight on the heterointerface properties of a passivated silicon heterojunction solar cell. It is shown that due to the high density of defect states in the amorphous layer the structure has to be analyzed as a heterojunction. Using the analysis, the following values have been determined: conduction-band offset of 0.13 eV, electron affinity of 3.92 eV, and density of defect states in the intrinsic amorphous silicon being that of 4.14 X 10 ²¹ m ^—3 .