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7 pages, 1263 KB

Open AccessProceeding Paper

Servo Motor Predictive Maintenance by Kafka Streams and Deep Learning Based on Acoustic Data

by Attila Aradi and Attila Károly Varga

Eng. Proc. 2025, 113(1), 1; https://doi.org/10.3390/engproc2025113001 (registering DOI) - 28 Oct 2025

Servo motors, which are critical for high-precision industrial applications, require predictive maintenance to minimize downtime, aligning with Industry 5.0’s human-centric manufacturing. This study presents a system for Delta servo motors using acoustic data. An ESP32 LyraT module streams audio via HTTP to a [...] Read more.

Servo motors, which are critical for high-precision industrial applications, require predictive maintenance to minimize downtime, aligning with Industry 5.0’s human-centric manufacturing. This study presents a system for Delta servo motors using acoustic data. An ESP32 LyraT module streams audio via HTTP to a server, which forwards it to Apache Kafka. Convolutional neural networks (CNNs) detect anomalies; Statistical Process Control (SPC) identifies early faults; and ARIMA, LSTM, and Prophet forecast maintenance. A device architecture with IP-based device ID and a GUI supports monitoring. Experiments with an ESP32 LyraT (Espressif Systems, Shanghai, China) monitoring Delta ASDA-A3 motors (Delta Electronics, Taipei, Taiwan) over 72 h achieved 91% anomaly detection accuracy for anomalous sounds, 84% early fault detection, and LSTM forecasting of MSE trends with MAE 0.0078 for 24 h predictions. The system supported 32 kB/s with <1% packet loss. The system offers accurate monitoring, advancing Industry 5.0. Future work will include vibration data and web dashboards. Full article

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8 pages, 1484 KB

Open AccessProceeding Paper

Advancing Lubrication Modeling: A Preliminary Study of Finite Element Solutions for Cavitation-Aware Reynolds Equation

by Balázs Pere and Martin Lénárt

Eng. Proc. 2025, 113(1), 2; https://doi.org/10.3390/engproc2025113002 (registering DOI) - 28 Oct 2025

Abstract

In the modern automotive industry, one of the most challenging tasks is minimizing energy losses caused by friction. Despite its significance, only a limited number of numerical simulation tools are available for effectively addressing lubrication-related problems. The accurate modeling of lubrication phenomena requires [...] Read more.

In the modern automotive industry, one of the most challenging tasks is minimizing energy losses caused by friction. Despite its significance, only a limited number of numerical simulation tools are available for effectively addressing lubrication-related problems. The accurate modeling of lubrication phenomena requires solving a specialized form of the Navier–Stokes equations, which accounts for cavitation effects within a thin fluid film. To address this, a finite element software is currently under development to solve the Reynolds equation while incorporating cavitation effect. This advanced tool enables the precise simulation of how the microgeometry of contacting surfaces influences the lubrication characteristics of the fluid film. By optimizing these surface features, the research aims not only to reduce energy dissipation but also to ensure the long-term durability of mechanical components. The findings obtained thus far demonstrate promising improvements in lubrication efficiency and structural longevity. These results, along with the methodological advancements, will be presented in detail at the upcoming conference. Full article

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7 pages, 1420 KB

Open AccessProceeding Paper

Estimation of the Range of a Light-Duty Commercial Vehicle

by Peter Harth and Anna Nagy

Eng. Proc. 2025, 113(1), 3; https://doi.org/10.3390/engproc2025113003 (registering DOI) - 28 Oct 2025

Abstract

Electric vehicles are now a completely familiar part of the street scene. Their number is determined by many factors, one of the most significant of which is price. With the ever-increasing supply, it is becoming increasingly difficult for buyers to decide between different [...] Read more.

Electric vehicles are now a completely familiar part of the street scene. Their number is determined by many factors, one of the most significant of which is price. With the ever-increasing supply, it is becoming increasingly difficult for buyers to decide between different vehicles, as in many cases, there is a minimal difference between the two vehicles. A parameter influencing such a purchase is the vehicle’s range. Experience shows that in many cases the range given in the catalogue and the real, available range differ significantly. The available range changes dynamically while driving, typically showing a decreasing value. This research describes a range estimation model that takes into account driving habits and temperature conditions. Full article

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9 pages, 925 KB

Open AccessProceeding Paper

Autonomous Vehicle Drifting Under Dynamically Changing Road Friction Using Adversarial Agents

by Szilárd Hunor Tóth and Zsolt János Viharos

Eng. Proc. 2025, 113(1), 5; https://doi.org/10.3390/engproc2025113005 (registering DOI) - 28 Oct 2025

Abstract

Autonomous vehicle control has undergone remarkable developments in recent years, especially in maneuvering at the limits of traction. These developments promise improved maneuverability and safety, but they also highlight a constant challenge: translating control strategies developed in simulation into robust, real-world applications. The [...] Read more.

Autonomous vehicle control has undergone remarkable developments in recent years, especially in maneuvering at the limits of traction. These developments promise improved maneuverability and safety, but they also highlight a constant challenge: translating control strategies developed in simulation into robust, real-world applications. The complexity of real-world environments, with their inherent uncertainties and rapid changes, poses significant obstacles for autonomous systems that need to dynamically adapt to unpredictable conditions, such as varying traction. The aim of this research is to investigate the effectiveness of robust adversarial reinforcement learning (RARL) for controlling circular drift maneuvers under dynamic road adhesion changes and uncertainties. The presented simulation results show that agents trained with RARL can enhance agents developed using only standard reinforcement learning techniques, where they were most critically vulnerable, such as sudden significant loss of traction during the drift initiation phase. This could present another step towards the application of more robust autonomous systems. Full article

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8 pages, 1700 KB

Open AccessProceeding Paper

An Eye-Tracking Analysis of Rider Behavior and Handling Strategy in Motorcycle Racing

by Michael Bohm and Jan Fojtasek

Eng. Proc. 2025, 113(1), 7; https://doi.org/10.3390/engproc2025113007 (registering DOI) - 28 Oct 2025

Abstract

This study focuses on the use of eye-tracking technology to analyse the rider’s visual attention during racing on a Ducati Panigale V2 motorcycle. Using the TOBII Pro Glasses 2 system, the rider’s gaze dynamics were recorded, including fixations, eye movements (saccades) and gaze [...] Read more.

This study focuses on the use of eye-tracking technology to analyse the rider’s visual attention during racing on a Ducati Panigale V2 motorcycle. Using the TOBII Pro Glasses 2 system, the rider’s gaze dynamics were recorded, including fixations, eye movements (saccades) and gaze distribution on key sections of the track. The results revealed a link between gaze stability and cornering efficiency, particularly in optimising braking points and selecting the ideal trajectory. Identifying unstable visual behavior—such as frequent gaze deviations or constant switching between reference points—provides valuable insights for improving driving technique. This approach confirms the importance of eye-tracking as a tool for objective evaluation and optimization of rider performance in motorsport. Full article

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8 pages, 586 KB

Open AccessProceeding Paper

Exploring the Link Between Ride-Sharing Experience and Autonomous Vehicle Acceptance in the Context of Sustainable Mobility

by Réka Koteczki, Zoltán Szávicza and Boglárka Eisinger Balassa

Eng. Proc. 2025, 113(1), 8; https://doi.org/10.3390/engproc2025113008 (registering DOI) - 28 Oct 2025

Abstract

Sustainable transportation systems are becoming an increasingly important issue around the world, especially with the advancement of urbanisation. Autonomous vehicles and ride-sharing services represent innovative mobility solutions that can improve not only the efficiency of transportation but also its environmental sustainability. The aim [...] Read more.

Sustainable transportation systems are becoming an increasingly important issue around the world, especially with the advancement of urbanisation. Autonomous vehicles and ride-sharing services represent innovative mobility solutions that can improve not only the efficiency of transportation but also its environmental sustainability. The aim of this study is to examine Hungarian consumers’ attitudes toward ride-sharing and their acceptance of AVs, with a focus on whether there is a link between the two phenomena. The research is based on a nationally representative sample of 2000 respondents. Correlation analyses were performed based on the dimensions of technology acceptance models. Based on the results, a significant positive correlation can be demonstrated between the willingness to use ride-sharing services in the future and the openness towards AVs. Perceived usefulness and social influence showed the strongest relationship with intention of usage. The results contribute to the social acceptance of autonomous technologies and sustainable transport in Hungary. Full article

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9 pages, 1359 KB

Open AccessProceeding Paper

Evaluation of SLAM Methods for Small-Scale Autonomous Racing Vehicles

by Rudolf Krecht, Abdelrahman Mutaz A. Alabdallah and Barham Jeries B. Farraj

Eng. Proc. 2025, 113(1), 9; https://doi.org/10.3390/engproc2025113009 (registering DOI) - 28 Oct 2025

Abstract

Simultaneous Localization and Mapping (SLAM) is a critical component of autonomous navigation, enabling mobile robots to construct maps while estimating their location. In this study, we compare the performance of SLAM Toolbox and Cartographer, two widely used 2D SLAM methods, by evaluating their [...] Read more.

Simultaneous Localization and Mapping (SLAM) is a critical component of autonomous navigation, enabling mobile robots to construct maps while estimating their location. In this study, we compare the performance of SLAM Toolbox and Cartographer, two widely used 2D SLAM methods, by evaluating their ability to generate accurate maps for autonomous racing applications. The evaluation was conducted using real-world data collected from a RoboRacer vehicle equipped with a 2D laser scanner and capable of providing odometry, operating on a small test track. Both SLAM methods were tested offline. The resulting occupancy grid maps were analyzed using quantitative metrics and visualization tools to assess their quality and consistency. The evaluation was performed against ground truth data derived from an undistorted photograph of the racetrack. Full article

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