Advances in Science and Technology Vol. 165

Abstract: The ballbar measurement process is a useful friendly and quick way to verify a machine tool’s accuracy. The ballbar is a telescopic linear sensor with two precision balls at both ends. During the measurement, the sensor’s end is precisely located with the support of the magnetic cups, resulting in the +/- micrometer accurate measurement of radius in each of points of circular path. A CNC machine’s accuracy could be affected with several defects like defective tooling, worn spindles, or issues with workpiece clamping. The errors related to the machine are known as geometric, play related or dynamic. These issues can occur at a used or at a brand new, or just a not properly installed machine too. These problems should be eliminated in order to ensure defect free machining. The values are available quickly and to decrease the uncertainty of the process the measurement can be performed on different parts of the machining table. As a result of the ballbar measurement the values are listed for each angle around a circle with the deviation from the theoretical perfectly round. The values give a proper overview of the condition of the machine. Based on the measurement results are connected to polar coordinates, which show the deviation in polar directions. The basic idea behind this study, that these values could be more useful for the everyday production processes than just machine state and maintenance related results. The main barrier is that the values are in polar coordinate till the programming running mostly in Cartesian coordinate system. Our study focuses on preparing an extended data processing to better understand the condition of the machine and use these values as support of the manufacture programs. The solution with the use of modern mathematical background ensures to use the values as corrections and to map the different locations of the workplace of the 3-axis machine.

Abstract: There are number of different methods and procedures in vibration analysis, where the natural frequencies of the specimen or the system are one of the key parameters. It is known that these frequencies can change under load, for example in response to pre-stressing, but the effect of residual stresses is less known. By developing a suitable method, natural frequencies can be used to predetermine residual stress, therefore this method can be used for example predicting whether it will cause deformation during machining of a part, whether it requires increased attention or how to set the parameters well for vibratory stress relief. The results can be significant cost and time savings, as well as the improvements of the quality. Natural frequency is the frequency of free vibration of an undamped linear vibration system, or in other words at which a system left alone will vibrate after excited by an external force [1]. Metal castings or welded structures may have several natural frequencies which appear as frequency bands or ranges on the measurement images. Based on these, to determine the natural frequency of a component or system, we need to excite a frequency as close as possible to the natural frequency for the resonance to occur. When the resonance is reached, the amplitude of the system is at its maximum, and the natural frequencies of the workpiece can be measured. Traditionally, sensors, usually accelerometers are used to measure the natural frequency. The continuous development of information technology has made it possible to replace these sensors with an acoustic diagnostic system. During this research, we have developed an acoustic diagnostic system and procedure, which can generate the acoustic measurement images. We have evaluated the measurement images in many ways, and many different types of components and materials (mostly iron alloys) were analyzed. In addition, the changes of natural frequencies show a similar pattern in the case of parts before treating with vibratory stress relief as for load tests.

Abstract: In digital image processing, artificial intelligence is increasingly applied to image analysis, enhancement, pattern recognition, object recognition, and classification. Unlike traditional image processing, which often relies on rules and predefined algorithms, AI-based approaches use learning, adaptation, and automatic decision-making to identify and manage image features. Key technologies include deep learning, neural networks, and machine learning-based algorithms. AI-driven technology is now present across an expanding range of fields and industries, significantly augmenting classical image processing methods or even replacing certain steps or sub-processes with the power of machine intelligence. The paper aims to highlight the opportunities and trends offered by artificial intelligence in the field of digital image processing.

Abstract: Machine learning models are effective tools for predicting and reducing noise levels in industrial gear systems. In this study, we compare different machine learning methods to investigate the effects of different gear modification parameters on noise levels. Four different predictive models was used. Random Forest Regressor, XGBoost, Gradient Boosting Machines and neural network. The study concluded that Random Forest and Gradient Boosting Machines models were the most effective. Both models achieved low mean squared error values 6.10 and 6.67. Further tests with synthetic data confirmed the stability of these models. Current sustainability trends show that the integration of machine learning into industrial applications fits well with manufacturers' objectives. However, it is currently challenging to determine which machine learning methods are most effective in optimizing noise reduction. This paper seeks to address this gap by comparing the accuracy and reliability of these models. Based on the results, the use of machine learning models is recommended to reduce noise levels in geared systems.

Abstract: We have long heard the terms sustainable future and renewable energy coming from many directions. But many people think of them as challenges for the future. However, robots, self-driving cars and smart houses around us also prove that the future is only a matter of hours. With artificial intelligence developing at a rapid pace, we need to find a safe way to cover our energy hunger. Currently, we can almost find fossil fuels or electrically powered vehicles on the roads. However, non-renewable energy sources are already being replaced by renewables day by day. However, renewable energy generated by the most used weather-dependent solar and wind farms has to be stored due to uneven energy use. The solution is not only batteries, but also hydrogen produced by water splitting. The energy storage potential of hydrogen lies in its high specific energy content, zero-emissions and can be produced in almost unlimited quantities. The domestic hydrogen strategy is also related to this, which provides guidance for phasing out diesel use as part of the transition to clean modes of transport. Reducing the carbon footprint of vehicle traffic from hydrogen use and extending hydrogen mobility to bus, train and waste transport requires the deployment of new hydrogen refueling infrastructure. However, these goals, new operating conditions and their integration into new applications that are in direct contact with consumers pose security challenges. Safety is the most important element for the smooth development and acceptance by society of new technologies. Therefore, in this article we will deal with the safety risks and dangers of hydrogen refueling stations. We describe the advantages, dangers and physical chemical properties of hydrogen. We present the system elements and operating principle of a hydrogen fueling station. Finally, we list the risk reduction measures and safety approaches that promote the safe design, operation and management of hydrogen-based technologies in Hungary.

Abstract: The integration of electric vehicles and vehicle-to-grid (V2G) systems is becoming more and more widespread around the world. This is particularly important for the expansion of sustainable energy applications. The aim of this paper is to present the integration potential of V2G systems. The authors focus on the Hungarian context, taking into account local regulations and specificities, including local infrastructure and economic constraints. The authors use a SWOT analysis in their work, which highlights the current (2024) V2G situation in Hungary. The analysis shows that the country has potentials in the field of electromobility, but there are also a number of constraints that the country's policymakers need to overcome for the future.

Abstract: In the competitive landscape of today's automotive industry, achieving excellence is fundamental. Our paper delves into the practical application of lean manufacturing principles within a given company.Drawing on collaborative efforts with industry experts and professionals, this paper documents practical experiences gained in the field of production management. By implementing various lean tools on one of the production lines in the company, significant strides were taken towards continuous improvement, resulting in enhanced productivity, reduced lead times, and heightened customer satisfaction.The study focuses on three key dimensions: engineering, economics, and management. The Plan-Do-Check-Act (PDCA) method was employed to establish performance indicators and guide continuous improvement initiatives. A 5S system was implemented to optimize the organization of high racks, resulting in a 3.7% reduction in downtime. Standardized work protocols were developed to promote one-piece flow, and operator cycle time balancing was applied to a specific component (Part X). These efforts led to a 30.77% improvement in productivity, with the line now producing 17 pieces of Part X per hour, compared to the previous output. The cycle time for producing one finished good part was reduced by 132 seconds, from 950 seconds to 818 seconds, and the welding robot was strategically designated as the bottleneck to further streamline operations.Economically, the reallocation of operators reduced Daily Labor Cost, creating a surplus of 0.64 hours (38 minutes) per operator per shift. This surplus allows operators to support other tasks or production lines, demonstrating the potential for ongoing resource optimization. Additionally, the integration of digitalized standardized work protocols facilitated training and streamlined production processes.The results highlight the transformative impact of lean manufacturing principles, including reduced downtime, improved cycle times, and optimized labor allocation. This study underscores the importance of lean tools in driving operational excellence and achieving measurable improvements in manufacturing efficiency, providing a practical framework for continuous improvement in the automotive industry.

Abstract: Intralogistics, involving the internal flow of goods and materials within warehouses and production facilities, plays a critical role in modern supply chains. The increasing complexity and diversity of material handling machines and warehousing environments present significant challenges in optimizing these systems. Traditional methods often fail to effectively integrate the wide range of machinery and dynamic warehousing conditions. This article first provides an overview of the current material handling systems, the latest trends and the applicable intelligent software and hardware components. Next, necessity of an AI-focused framework is justified. Finally, a customized AI-focused framework is presented for a material handling and warehousing case. Applicability of the framework is demonstrated through a graph-matching example. The paper ends with a summary and outlook section.

Abstract: The literature review will focus on the relevance of the research, review of scientific papers, and mapping of publications. Understanding the relevant knowledge requires exploration, statistical, content and evaluation analyses which help the researcher to identify future research directions and problematical areas. Systematic literature review (SLR) is a scientific research approach that focuses specifically on relevant professional knowledge and their lessons to establish the research objectives. The design of electromobility and logistics in finished product distribution necessitates the formulation of a novel, more rigorous, and methodologically robust systematic literature review to delineate the scope of professional research, design, and education.

Abstract: This paper presents a qualitative, descriptive study addressing the imperative of developing multidisciplinary skills in STEM education to prepare students for the challenges of Industry 4.0. Acknowledging the limitations of traditional analytical and technical training, the study focuses on the significance of teamwork and the combination of multiple STEM skills. Multidisciplinary programming projects can be used effectively in higher education to facilitatie cross-disciplinary collaboration among students. Our pilot project, presented in this paper, uniquely contributes to this discourse by integrating logistics, robotics, and programming into a graphical simulation software that represents the 3D model of a warehouse with a programmable forklift truck tasked with navigating and transporting parcels. This paper discusses the methodology, outcomes, and implications of the pilot project, highlighting its role in preparing STEM students for the complex challenges of an interconnected world and Industry 4.0.