This paper focuses on the implementation of Industry 4.0-based maintenance approaches using AR technology. The primary objective is to highlight the benefits of AR in maintenance and provide a framework for implementing AR-based maintenance systems. AR can assist maintenance personnel in performing maintenance tasks more efficiently by providing real-time instructions and information. By overlaying virtual information on real equipment, AR can guide maintenance personnel through maintenance procedures, reducing human errors and improving the quality of the work. AR can also provide remote assistance to maintenance personnel, allowing experts to remotely guide less experienced personnel through complex maintenance procedures. The proposed application provides motorcycle service assistance using AR and telepresence technologies. It enables remote monitoring of the service flow and performs an automated identification of parts. The system provides instructions for disassembling more complex mechanical components, provides a 3D model of the object, and enables ordering of the necessary parts.

Striving for excellence during the assembling process through incorporating the expectations of Industry 4.0 requires complex information management on issues of overall system status, especially the physical characteristics and position of the parts being assembled, as well as the assembling units and tools. This research introduces both an overall customized assembling system supervision model, which is based on a modified four-layer control system hierarchy that suits the specific requirements of such systems and the developed wireless sensor network technology for assembling process management with a particular focus on localization. The developed model highlights the localization problems of the system as well as other aspects required for overall system status determination. The localization of assembled parts is based on the fingerprint localization method by using the received signal strength indicator. The proposed localization algorithms are based either on artificial neural networks or on the weighted k-nearest neighbor method. The developed model has been tested both in laboratory conditions and in a simulated industrial environment. The research results offer a general solution to the problem of assembling system supervision, regardless of size and shape, with emphasis on the localization problem solution.

Reliable positioning performance is crucial in precision industrial automation, especially under dynamic conditions. This research focuses on examining the accuracy of a toothed belt driven linear servo motor positioning system, with the aim of identifying the main factors influencing position deviation. The system was built on a Power Belt ITO 060M shaft, controlled by an Rtelligent RS200-G servo controller and an Omron CP1L-E PLC. Position measurement was performed by a laser distance meter and a Cognex IS2000C-130-40-SR8 industrial camera, both calibrated with certified gauge blocks. The linear unit was moved to predefined points at different speeds, accelerations, and decelerations profiles and the resulting position deviation was recorded for each case. Several analytical methods were used to evaluate the collected measurement data to determine which factors have the greatest impact on positioning error. The result showed that speed significantly affected the accuracy of the system, while the effects of deceleration and acceleration were less pronounced. The study contributes to the fine-tuning of linear motion system and the targeted improvement of their performance.

The overall well-being and productivity of crops rely on a series of interconnected processes throughout their entire growth cycle. Among these processes, the quality of spraying plays a pivotal role in maintaining crop health and ensuring increased productivity. An Unmanned Aerial Vehicles assisted particle spraying system in agriculture involves the use of Unmanned Aerial Vehicles equipped with specialized equipment to distribute particles such as plant protection products, fertilizers, or other agricultural inputs over crops. This technology offers several advantages over traditional ground-based methods, including increased efficiency, precision, and reduced environmental impact. The effectiveness of spraying, in turn, hinges on various factors, one of which is the even distribution of spraying droplets. Consequently, there exists a need for a reliable, consistent, precise, and accurate automated method to assess the parameters governing this distribution. In this study, a methodology is introduced for evaluating the quality of plant spraying, and the results of this method's testing are presented. Data is gathered by employing water-sensitive papers positioned on the crops, which are then scanned using an industrial-grade camera. Subsequently, this data undergoes processing through image analysis algorithms using Matlab. The outcomes of the research demonstrate the robustness of the proposed methodology in obtaining the essential data required for determining spraying distribution. Compared to existing solutions, the presented approach offers increased reliability, consistency, precision, and automation, thereby addressing the need for a more reliable and accurate method of assessing spraying quality in agriculture.