Quality control of welded joint is an indispensable part of the welding production process. As part of spot resistance welding group, cross-wire welding process showed great application for welding of products for everyday usage. The non-contact quality control checking is fit for purpose due to specific characteristics of welded products that consist of two cross welded wires or a combination of wires and strips. This work proposes a new method for detecting and measuring of required dimensional parameters, but also founds its applicability for other products if required. A crucial parameter of this research is the height of welded joint, which is necessary for calculating the penetration of the wire into the wire. The proposed measuring method with a reconfigurable measuring system is explained in this paper. The main component of this system is using a machine vision system, which has become an indispensable part of industrial metrology and is considered one of the industry 4.0 concepts. The calibration process for such systems could be very complicated. This work shows an elaborated calibration procedure for this kind of measuring system with referenced standards made for this purpose. Measurement results are compared with ones obtained by conventional method. The focus of vision system is a substantial part as it dictates the quality of the system. This research is done within the project in collaboration with the industrial sector and all samples are from real processes. The results of measured penetration on one product group are showing the applicability of a reconfigurable measuring system in the welding sector, and demonstrate that measurement of welding penetration based on machine vision is feasible and can ensure accuracy.

Modern industry is increasingly relying on digital technologies to optimize maintenance processes, reduce costs and ultimately increase productivity. Conventional maintenance models, such as corrective and preventive maintenance, often lead to unnecessary downtime and high operating costs. Predictive Maintenance (PdM), which is based on data analysis and artificial intelligence (AI), enables the timely detection of failures and the optimization of maintenance cycles and is therefore a key component of modern industry. With the advancement of artificial intelligence (AI) and machine learning, data analytics can accurately predict failures, thereby reducing the need for preventive and corrective maintenance in the form that was common before the application of AI. Predictive maintenance (PdM) is emerging as a key element of modern industry, enabling a significant reduction in downtime, an increase in operational efficiency and a reduction in maintenance costs. This paper explores the application of artificial intelligence, including machine learning (ML), deep learning (DL) and the Internet of Things (IoT) in predictive industrial maintenance, analyzes the key implementation challenges in implementation, considers the potential benefits for industrial systems and discusses the challenges and prospects for the further development of this approach.