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GPS Based Navigation and Mobile Base Station of a Mobile Robot Platform

Rudolf Krecht Norbert Boros Dániel Eredits

Publication Name: Chemical Engineering Transactions

Publication Date: 2023-01-01

Volume: 107

Issue: Unknown

Page Range: 385-390

Description:

One of the central challenges of today is to increase the degree of automation of the available systems and equipment in a sustainable way. This paper presents the development of an outdoor mobile robot platform navigation method, including an on-board unit and a mobile base station to support the operation of the ZalaZone Automotive Proving Ground in Zalaegerszeg, Hungary, by precise, automated deployment of traffic cones. Currently, traffic cone handling is a time- and labor-intensive job. Its automation saves a significant amount of human resources. As a first step, the technical requirements of the problem have been defined. Then, the navigation system that met the pre-defined requirements has been implemented. This paper presents the detailed development of this navigation system, including a description of the test phases as well. As part of the development process, the operation of a Global Navigation Satellite System (GNSS) receiver corrected by Real-Time Kinematic (RTK) is presented, supported by a small, low-power mobile base station, followed by a description of the applied hardware and software components, alongside with the alignment and further development of these components to achieve centimeter-accurate positioning. The Network Transport of Radio Technical Commission for Maritime Services (RTCM) via Internet Protocol (NTRIP) server facilitates the communication between the onboard unit and the base station via a 5G network. This work is continued by a detailed description of the autopilot system on the robotic platform, including the tools and software used for this purpose. The calibration process of the navigation system is described as well. Finally, the results and observations gained during the test are summarized and analyzed. These results have shown that the addition of an RTK system has highly increased the accuracy of a general GNSS receiver. In addition, these results underline the crucial role of 5G networks in the case of automated mobile applications.

Open Access: Yes

DOI: 10.3303/CET23107065

From Agricultural and Forest Land Development to Urban Landscapes: Green Energy's Influence on Global Pollutant Emissions

Laszlo Vasa Irum Shahzadi Urszula Mentel Muhammad Adnan Bashir Irum Shahzadi Li Qing

Publication Name: Land Degradation and Development

Publication Date: 2025-08-15

Volume: 36

Issue: 13

Page Range: 4672-4690

Description:

There is a sharp inclination to use green energy sources such as solar, hydro, and nuclear energy to accomplish the COP29 targets and sustainability goals. The current study attempts to explore the role of green solar, hydro, and agriculture land use apropos global pollutant emissions. In doing so, the study examines the impacts of agricultural land use, forest area, and urbanization on global emissions. The study uses the global historical data from 1990Q1 to 2021Q4. The authors employ the diagnostic tests, autoregressive distributed lag models, and causality analysis for empirical analysis. The autoregressive distributed lag model's results mentioned that agricultural land and forestry also help improve environmental sustainability and urban landscape in the short and long run. In addition, the results find linear and nonlinear impacts of green solar and nuclear energy to mitigate the global carbon emission levels. The structural change policies of industrialization and urbanization remain the critical obstacles to attaining environmental sustainability. The on-hand research contributes to the ongoing challenges faced by global economies regarding green energy sources, agriculture land management and their criticality in attaining a sustainable environment by reducing carbon emissions. The research recommends further investments in green solar, agriculture land management, and incentivizing clean energy sources to achieve sustainable global development.

Open Access: Yes

DOI: 10.1002/ldr.5661

Positive Impact of Waste Management Strategies and Decision Analysis with Intuitionistic Fuzzy Sugeno-Weber Aggregation Operators

Dragan Pamucar Shi Yin Abrar Hussain Kifayat Ullah Vladimir Simic Yong Deng

Publication Name: Boletim Da Sociedade Paranaense De Matematica

Publication Date: 2025-08-13

Volume: 43

Issue: 3

Page Range: Unknown

Description:

Waste management is a crucial and significant subject that has gained much attention globally because it has several environmental, social, financial and economic implications. Solid waste management is a very challenging task for clean urban and rural societies. We studied some reliable strategies for handling the waste materials and garbage produced by people. To serve this purpose, an intuitionistic fuzzy set (IFS) is a well-known model used for modeling and processing unpredictable information and providing accurate approximated results in the decision-making process. Power average operators allow the interrelationship of the input arguments and deal with uncertain information in complicated situations. This article expresses Sugeno-weber triangular norms under intuitionistic fuzzy (IF) information. We developed a class of new aggregation operators, including intuitionistic fuzzy Sugeno-Weber power-weighted average (IFSWPWA) and intuitionistic fuzzy Sugeno-Weber power-weighted geometric (IFSWPWG) operators. It is observed that both the newly proposed operators satisfy the properties of aggregation. The multi-criteria decision-making (MCDM) problem is proposed to evaluate real-life applications and numerical examples. An experimental case study under the system of waste materials is considered in the article to reveal the intensity and applicability of derived approaches. The comparison analysis and sensitivity analysis show the significance of our proposed work.

Open Access: Yes

DOI: 10.5269/bspm.79085

Synergistic Effects of CuO and ZnO Nanoadditives on Friction and Wear in Automotive Base Oil †

I. Szabó Rafiul Hasan

Publication Name: Applied Sciences Switzerland

Publication Date: 2025-08-01

Volume: 15

Issue: 15

Page Range: Unknown

Description:

Efficient lubrication lowers friction, wear, and energy losses in automotive drivetrain components. Advanced lubricants are key to sustainable transportation performance, durability, and efficiency. This study analyzes the tribological performance of Group III base oil with CuO and ZnO nanoadditive mixtures. These additives enhance the performance of Group III base oils, making them highly relevant for automotive lubricant applications. An Optimol SRV5 tribometer performed ball-on-disk sliding contact tests with 100Cr6 steel specimens subjected to a 50 N force and a temperature of 100 °C. The test settings are designed to mimic the boundary and mixed lubrication regimes commonly seen in the automobile industry. During the tests, the effect of nanoparticles on friction was measured. Microscopic wear analysis was performed on the worn specimens. The results demonstrate that adding 0.3 wt% CuO nanoparticles to Group III base oil achieves a 19% reduction in dynamic friction and a 47% decrease in disk wear volume compared to additive-free oil. Notably, a 2:1 CuO-to-ZnO mixture produced synergy, delivering up to a 27% friction reduction and a 54% decrease in disk wear. The results show the synergistic effect of CuO and ZnO in reducing friction and wear on specimens. This study highlights the potential of nanoparticles for lubricant development and automotive applications.

Open Access: Yes

DOI: 10.3390/app15158258

Sustainability Approach of SAP Application Management Service Solutions in the Field of Warehouse Management

Ferenc Erdős Rahma Farhat

Publication Name: Chemical Engineering Transactions

Publication Date: 2023-01-01

Volume: 107

Issue: Unknown

Page Range: 259-264

Description:

Organizations using SAP systems encounter the challenge of enhancing services while minimizing costs and response times. To achieve this, SAP offers a comprehensive range of sustainable cloud solutions in the field of Application Management Services (AMS), integrating machine learning, big data, and smart-machine technologies. This research endeavors to offer an alternative, sustainable approach, utilizing non-cloud-based machine learning to improve the efficiency of ticket classification in SAP AMS. This study presents an SAP Extended Warehouse Management (EWM) related case study in which various machine learning algorithms were applied to real-world SAP ticket data to automatically categorize incident tickets. As warehousing plays a major role in streamlining the supply chain, this approach aligns with the goals of sustainable supply chain process optimization. During the analysis, various classification algorithms were compared to achieve the best metrics. Our research did not just analyze the different algorithms for that specific business problem. The best model was integrated and deployed as well to accomplish a sustainable SAP AMS solution in the field of EWM.

Open Access: Yes

DOI: 10.3303/CET23107044

Investigation of Turbocharger Compressor Wheel Damage Due to Collision with Condensed Water Droplets

I. Zsoldos A. L. Nagy Richárd Takács

Publication Name: Lecture Notes in Mechanical Engineering

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: 575-581

Description:

The compressor wheels used in turbochargers have typically been made of aluminium alloy for decades. The primary reason is to achieve the lowest possible rotor inertia. However, while in the past this component was only encountered with filtered air, nowadays, due to developments in compliance with tightening emission standards, various fluids also collide with the spinning blades, which can cause mechanical damage. One such fluid is condensed water in the low-pressure exhaust gas channel (LP-EGR) formulated at cold starts and low-load conditions. This kind of design has been developed to reduce the nitrogen oxide emission and is used in both Otto and Diesel engines. This paper presents a testing method - implemented on a component testbench - and its results for this phenomenon. First, the effect of the volume flow of the condensed water colliding with the spinning blades was analyzed, and then, in addition to a constant volume flow, the speed dependence of the degree of damage was also determined. Next to the visual inspections, the physical changes on the blades were also detected by vibration diagnostic tools, mainly by analyzing the amplitude of the order correlated to the number of blades, from which we can deduce the changed balance level of the rotor.

Open Access: Yes

DOI: 10.1007/978-3-031-15211-5_48

Investigation of the Load-Bearing Capacity of Resin-Printed Components Under Different Printing Strategies

Szabolcs Fischer Szabolcs Szalai Brigitta Fruzsina Szívós Vivien Nemes

Publication Name: Applied Sciences Switzerland

Publication Date: 2025-08-01

Volume: 15

Issue: 15

Page Range: Unknown

Description:

This study examines the influence of different printing orientations and infill settings on the strength and flexibility of components produced using resin-based 3D printing, particularly with masked stereolithography (MSLA). Using a common photopolymer resin and a widely available desktop MSLA printer, we produced and tested a series of samples with varying tilt angles and internal structures. To understand their mechanical behavior, we applied a custom bending test combined with high-precision deformation tracking through the GOM ARAMIS digital image correlation system. The results obtained clearly show that both the angle of printing and the density of the internal infill structure play a significant role in how much strain the printed parts can handle before breaking. Notably, a 75° orientation provided the best deformation performance, and infill rates between 60% and 90% offered a good balance between strength and material efficiency. These findings highlight how adjusting print settings can lead to stronger parts while also saving time and resources—an important consideration for practical applications in engineering, design, and manufacturing.

Open Access: Yes

DOI: 10.3390/app15158747

Machine learning models for the elastic-critical buckling moment of sinusoidal corrugated web beam

Andreas Müller Nurullah Bektaş G. Hajdú

Publication Name: Results in Engineering

Publication Date: 2024-09-01

Volume: 23

Issue: Unknown

Page Range: Unknown

Description:

The torsional stiffness of I-beams with sinusoidal corrugated web is higher than that of flat web beams and the accuracy of the available hand-calculation methods to determine the elastic critical lateral-torsional buckling moment depends on the geometrical parameters of the beam and the web corrugation. This study proposes different machine learning models to determine the elastic lateral-torsional buckling moments of corrugated web beams. Various machine-learning algorithms such as Decision Tree, Random Forests, Gradient Boosting, Support Vector Regression, Catboost, and Deep Neural Network were employed to develop and train for predicting the elastic-critical lateral-torsional buckling moments of I-beams with corrugated web. An extensive dataset with 2250 pieces was constructed using linear buckling analyses on full-shell finite element models to determine the elastic-critical buckling moment of simply supported beams with sinusoidal web corrugation. Based on the statistical parameters of the predicted and test data, the accuracy and safety assessment of the different machine learning models are examined. The accuracy of the available hand-calculation methods is also investigated. The results of the parametric study showed that the overall performance of the different machine learning models is promising, although, not all are directly suited for the described problem.

Open Access: Yes

DOI: 10.1016/j.rineng.2024.102371

Data-Driven Predictive Modeling for Investigating the Impact of Gear Manufacturing Parameters on Noise Levels in Electric Vehicle Drivetrains

Krisztián Horváth

Publication Name: World Electric Vehicle Journal

Publication Date: 2025-08-01

Volume: 16

Issue: 8

Page Range: Unknown

Description:

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance.

Open Access: Yes

DOI: 10.3390/wevj16080426