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.

This paper introduces an innovative algorithm aimed at enhancing robot learning using dynamic trajectory modeling and time-dependent state analysis. By integrating reinforcement learning (RL) and trajectory planning, the proposed approach enhances the robot’s adaptability in diverse environments and tasks. The framework begins with a comprehensive analysis of the robot’s operational space, focusing on Cartesian coordinates and configuration systems. By modeling trajectories and states within these systems, the robot achieves sequential tracking of arbitrary states, facilitating efficient task execution in various scenarios. Experimental results demonstrate the algorithm’s efficacy in manipulation tasks and path planning in dynamic environments. By integrating dynamic trajectory modeling and time-dependent state analysis, the robot’s adaptability and performance improve significantly, enabling precise task execution in complex environments. This research contributes to advancing robot learning methodologies, particularly in human–robot interaction scenarios, promising applications in manufacturing, healthcare, and logistics.

The advent of the Internet of Things (IoT) has opened up new opportunities in education, particularly in the field of mobile robotics. Mobile robotics is a fast-growing field, and with the increasing demand for skilled robotics engineers, it is essential to provide students with hands-on experience in designing and developing robots. In this regard, an IoT-based 3D printed mobile robot platform has been developed for students training. STEM (Science, Technology, Engineering, and Mathematics) education has been gaining attention as a means to improve science and engineering education. STEM education aims to foster critical thinking, problem-solving skills, creativity, and innovation among students. It involves an educational policy that aims to develop the next generation of skilled professionals by integrating the fields of Science, Technology, Engineering and Mathematics. The goal of this approach is to provide students in mechatronics, who are assigned to the robotics lab during their studies, an up to date knowledge and experience in research and development activities. Thus, this paper aims to provide education that focuses on the acquisition and application of engineering knowledge, including control engineering, mechanical engineering, electrical and electronic engineering, information engineering, among others, with the main focus on mobile robot research.

Three-dimensional food printing (3DFP), a specialized application of additive manufacturing (AM), employs a layer-by-layer deposition process guided by digital image files to fabricate edible structures. Utilizing heavily modified 3D printers and Computer-Aided Design (CAD) software technology allows for the precise creation of customized food items tailored to individual aesthetic preferences and nutritional requirements. Three-dimensional food printing holds significant potential in revolutionizing the food industry by enabling the production of personalized meals, enhancing the sensory dining experience, and addressing specific dietary constraints. Despite these promising applications, 3DFP remains one of the most intricate and technically demanding areas within AM, particularly in the context of modern gastronomy. Challenges such as the rheological behaviour of food materials, print stability, and the integration of cooking functions must be addressed to fully realize its capabilities. This article explores the possibilities of applying classical modified 3D printers in the food industry. The behaviour of certain recipes is also tested. Two test case scenarios are covered. The first scenario is the work and formation of a homogenized meat mass. The second scenario involves finding a chocolate recipe that is suitable for printing relatively detailed chocolate decorative elements. The current advancements, technical challenges, and future opportunities of 3DFP in the field of engineering, culinary innovation and nutritional science are also explored.

This paper’s goal is to introduce the third step of the EFOP-3.6.1-16-2016-00014. project on the Faculty of Engineering, University of Szeged. In this period the production technology of composite material was chosen and a fatigue test machine was developed and tested. The paper shortly describes the composite materials and summarizes the theory of fatigue than it presents the process of the development with several prototypes of fatigue test machine, some of which were manufactured and tested. Initially a shaker played the key role in the first two conceptions and finally a crank mechanism became as the best solution. The main solved problems during the development were selection of bearings and solving the partly dynamic balancing of the moving parts.