Krisztián Horváth

59224275100

Publications - 26

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

The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives

Dániel Feszty Krisztián Horváth

Publication Name: Applied Sciences Switzerland

Publication Date: 2025-07-01

Volume: 15

Issue: 14

Page Range: Unknown

Description:

Gear whine noise is governed not only by intentional microgeometry modifications but also by unavoidable pitch (indexing) deviation. This study presents a workflow that couples a tooth-resolved surface scan with a calibrated pitch-deviation table, both imported into a multibody dynamics (MBD) model built in MSC Adams View. Three operating scenarios were evaluated—ideal geometry, measured microgeometry without pitch error, and measured microgeometry with pitch error—at a nominal speed of 1000 r min−1. Time domain analysis shows that integrating the pitch table increases the mean transmission error (TE) by almost an order of magnitude and introduces a distinct 16.66 Hz shaft order tone. When the measured tooth topologies are added, peak-to-peak TE nearly doubles, revealing a non-linear interaction between spacing deviation and local flank shape. Frequency domain results reproduce the expected mesh-frequency side bands, validating the mapping of the pitch table into the solver. The combined method therefore provides a more faithful digital twin for predicting tonal noise and demonstrates why indexing tolerances must be considered alongside profile relief during gear design optimization.

Open Access: Yes

DOI: 10.3390/app15147851

Using Machine Learning Models to Predict and Reduce Noise Levels in Gear Systems

Ambrus Zelei Krisztián Horváth

Publication Name: Advances in Science and Technology

Publication Date: 2025-01-01

Volume: 165 AST

Issue: Unknown

Page Range: 215-221

Description:

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.

Open Access: Yes

DOI: 10.4028/p-0GDArj

Development of a multibody model for go-karts considering frame flexibility

Ambrus Zelei Krisztián Horváth

Publication Name: Pollack Periodica

Publication Date: 2024-10-16

Volume: 19

Issue: 3

Page Range: 66-73

Description:

This study focuses on the optimization dynamics of racing go-karts, which is heavily influenced by the frame's stiffness. Lacking suspensions and differentials, go-karts rely on the frame stiffness for wheel balancing and skid prevention by lifting the inner rear wheel during turns. Utilizing a rigid-flexible model in MSC Software ADAMS View, validated by frame deformation measurements, this research integrates finite element analysis with multibody techniques. The model, leverages computer aided design files for frame geometry and employs finite element analysis for frame validation. It facilitates evaluating go-kart dynamics through simulations, aiding in maneuver testing and design optimization. This approach provides a comprehensive framework for advancing go-kart designs.

Open Access: Yes

DOI: 10.1556/606.2024.01050

Simulating Noise, Vibration, and Harshness Advances in Electric Vehicle Powertrains: Strategies and Challenges

Ambrus Zelei Krisztián Horváth

Publication Name: World Electric Vehicle Journal

Publication Date: 2024-08-01

Volume: 15

Issue: 8

Page Range: Unknown

Description:

This study examines the management of noise, vibration, and harshness (NVH) in electric vehicle (EV) powertrains, considering the challenges of the automotive industry’s transition to electric drivetrains. The growing popularity of electric vehicles brings new NVH challenges as the lack of internal combustion engine noise makes drivetrain noise more prominent. The key to managing NVH in electric vehicle powertrains is understanding the noise from electric motors, inverters, and gear systems. Noise from electric motors, mainly resulting from electromagnetic forces and high-frequency noise generated by inverters, significantly impacts overall NVH performance. This article details sources of mechanical noise and vibration, including gear defects in gear systems and shaft imbalances. The methods presented in the publication include simulation and modeling techniques that help identify and solve NVH difficulties. Tools like multi-body dynamics, the finite element method, and multi-domain simulation are crucial for understanding the dynamic behavior of complex systems. With the support of simulations, engineers can predict noise and vibration challenges and develop effective solutions during the design phase. This study emphasizes the importance of a system-level approach in NVH management, where the entire drivetrain is modeled and analyzed together, not just individual components.

Open Access: Yes

DOI: 10.3390/wevj15080367

Noise Reduction Methods in the Vehicle Industry: Using Vibroacoustic Simulation for Sustainability

Ambrus Zelei Krisztián Horváth

Publication Name: Chemical Engineering Transactions

Publication Date: 2024-01-01

Volume: 114

Issue: Unknown

Page Range: 763-768

Description:

To achieve sustainability goals, such as greenhouse gas emissions and environmental noise reduction, continuous innovation plays a key role in the vehicle industry. The noise emitted by vehicles negatively impacts both the environment and public health, making the development of noise reduction strategies crucial. Vibroacoustic simulation methodologies provide an opportunity to optimise vehicle power transmission systems by reducing the emitted noise level. Besides, the energy efficiency and performance of the vehicles can be improved by vibroacoustic simulations. In this research, a vibroacoustic simulation methodology is presented, focusing on the power transmission systems of vehicles. This approach integrates the Finite Element Method and Multibody Dynamics simulations with vibroacoustics to identify and redesign noisy components even during the conceptual design stage. This approach tackles the challenge of high-frequency tonal noise for electric vehicles, using psychoacoustic reviews to enhance passenger comfort. Key tasks involve electromagnetic force analysis in the drivetrain, structural vibration simulations, and noise reduction strategy optimisation using machine learning algorithms to reduce the reliance on physical prototypes. Capturing the current momentum of the industry, machine learning capabilities in vibroacoustic models can help engineers identify sources and eliminate or mitigate noise in the early design phase. Reducing the number of prototypes leads to more sustainable design processes. Our study shows the noise level can be reduced by 3-5 dB. This is particularly important in the context of electric vehicles, where high-frequency tone noise should be reduced, benefiting both passengers and their environment. Improving these factors is in line with the goals of the United Nations and improves the quality of urban life. Our research highlights the importance of vibroacoustic simulation and opens new directions in the field of noise reduction, promoting the spread of sustainable transport solutions.

Open Access: Yes

DOI: 10.3303/CET24114128

Gearbox Fault Diagnosis Using Industrial Machine Learning Techniques †

Ambrus Zelei Krisztián Horváth

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

This paper highlights the need for precise and reliable diagnostic methods for early fault detection in gearbox systems, something critical for industrial maintenance. Advances in machine learning (ML) and image processing have opened new avenues for diagnosis. This study explores ML techniques, particularly edge detection and maximized pooling, with the Inverse Distance Weighting method, for diagnosing gearbox faults from vibration signal images. Using the ODYSSEE-A Eye platform, a model was developed that achieved 96% accuracy in identifying faults from a 500-sample dataset. The research results promote further investigation and progress in this area, indicating specific possible directions for further research.

Open Access: Yes

DOI: 10.3390/engproc2024079036

Literature Review of Vibroacoustic Simulation in Geared Vehicle Power Transmission Systems for the Reduction of Radiated Noise

Ambrus Zelei Krisztián Horváth

Publication Name: Advances in Science and Technology

Publication Date: 2024-01-01

Volume: 153

Issue: Unknown

Page Range: 89-98

Description:

The radiated noise reduction of vehicular power transmission systems is one of the most actively researched areas. Noise not only impacts the comfort and safety of the driver and passengers but also regulated by the legislators. The simulation-based prediction of radiated noise of gear-drives is a rapidly evolving area and combines gear meshing models, finite element analysis, multibody dynamics and airborne noise simulation tools. The interfacing of these tools makes virtual noise prediction challenging. In this research, we conducted a literature review on vibroacoustic simulations, with a particular focus on reducing noise in power transmission systems. Based on the reviewed articles, it became evident that, although numerous measurement data are available, the usability of the data is limited. Most research focuses on individual stages of the structure and on smaller-sized powertrains. The measurement methods contain abundant valuable information; however, the literature lack of comprehensive articles that track the simulation process from the inception of excitation to body and air noises. Moreover, the majority of articles investigate the relationship between transmission error and NVH, considering it as a primary source of noise. New methodological approaches, such as the application of FEM meshes on gears, open new horizons in this domain. Throughout the literature review, we compiled potential noise-reduction solutions and highlighted directions for future methodology development research.

Open Access: Yes

DOI: 10.4028/p-Ucpx27

Predicting Natural Frequencies of a Cantilever Using Machine Learning

Ambrus Zelei Krisztián Horváth

Publication Name: Advances in Transdisciplinary Engineering

Publication Date: 2024-01-01

Volume: 59

Issue: Unknown

Page Range: 105-111

Description:

In the context of structural analysis and design, natural frequencies play a vital role, and their prediction is essential in machine and vehicle design processes. The simulations related to the modal parameters are computationally intensive for systems with large complexity. This paper demonstrates on an illustrative academic example that natural frequencies can be successfully predicted using ML models. This paper aims to develop a model based on machine learning (ML) to predict a simple cantilever's natural frequencies based on the physical parameters of the beam. The independent variables X are the geometric parameters including width, length, and thickness, while the dependent variable Y is the natural frequency. The study is framed using a systematic methodology that covers the stages of data collection, ML model selection, model training and validation. The validation process proves the effectiveness of ML as a computationally cheap replacement for traditional methods of prediction. The current research contributes to the investigation of the usage of commercially available ML tools in structural engineering. We report that the ODYSSEE A-Eye software is capable of natural frequency prediction with a varying geometry structure with less than 4% error for an 80-member training set of cantilever beam with various dimensions. Further developments will include considerations of noise, vibration, and harshness (NVH) to enhance system performance and improve user comfort.

Open Access: Yes

DOI: 10.3233/ATDE240533

Surface Waviness of EV Gears and NVH Effects—A Comprehensive Review

Dániel Feszty Krisztián Horváth

Publication Name: World Electric Vehicle Journal

Publication Date: 2025-09-01

Volume: 16

Issue: 9

Page Range: Unknown

Description:

Electric vehicle (EV) drivetrains operate at high rotational speeds, which makes the noise, vibration, and harshness (NVH) performance of gear transmissions a critical design factor. Without the masking effect of an internal combustion engine, gear whine can become a prominent source of passenger discomfort. This paper provides the first comprehensive review focused specifically on gear tooth surface waviness, a subtle manufacturing-induced deviation that can excite tonal noise. Periodic, micron-scale undulations caused by finishing processes such as grinding may generate non-meshing frequency “ghost orders,” leading to tonal complaints even in high-quality gears. The article compares finishing technologies including honing and superfinishing, showing their influence on waviness and acoustic behavior. It also summarizes modern waviness detection techniques, from single-flank rolling tests to optical scanning systems, and highlights data-driven predictive approaches using machine learning. Industrial case studies illustrate the practical challenges of managing waviness, while recent proposals such as controlled surface texturing are also discussed. The review identifies gaps in current research: (i) the lack of standardized waviness metrics for consistent comparison across studies; (ii) the limited validation of digital twin approaches against measured data; and (iii) the insufficient integration of machine learning with physics-based models. Addressing these gaps will be essential for linking surface finish specifications with NVH performance, reducing development costs, and improving passenger comfort in EV transmissions.

Open Access: Yes

DOI: 10.3390/wevj16090540

Noise, Vibration, and Harshness (NVH) Challenges in Hydrogen Internal Combustion Engine Vehicles

Krisztian Horvath Krisztián Horváth

Publication Name: Energy Science and Engineering

Publication Date: 2026-02-01

Volume: 14

Issue: 2

Page Range: 1067-1080

Description:

This paper presents a state-of-the-art literature review on noise, vibration, and harshness (NVH) in hydrogen-fuelled internal combustion engines. Studies published between 2011 and 2025 were screened, covering fundamental flame physics, test-bench work, and recent prototype vehicles. The review links hydrogen's core properties—high flame speed, wide flammability, low ignition energy, strong diffusivity—to specific NVH outcomes such as rapid pressure rise, knock, back-fire, and block resonance. For each pathway we summarise measured noise levels, vibration signatures, and psycho-acoustic findings. Mitigation methods are then grouped: lean premixing, direct injection, adaptive ignition timing, exhaust tuning, and structural damping. Results show that, with these measures, hydrogen engines can approach the NVH envelope of modern gasoline units. Remaining gaps lie in long-term durability under high-frequency loading and in full-vehicle sound quality. Overall, the review clarifies current knowledge, highlights consistent trends, and points to research still needed for quiet, smooth hydrogen mobility.

Open Access: Yes

DOI: 10.1002/ese3.70400

Data-Driven Prediction of Kinematic Transmission Error and Tonal Noise Risk in EV Gearboxes Based on Manufacturing Tolerances

Martin Kaszab Krisztián Horváth

Publication Name: Applied Sciences Switzerland

Publication Date: 2025-10-01

Volume: 15

Issue: 19

Page Range: Unknown

Description:

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R2 ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R2 ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment.

Open Access: Yes

DOI: 10.3390/app151910460

Digital Twin Approaches for Gear NVH Optimization: A Literature Review of Modeling, Data Integration, and Validation Gaps

Ambrus Zelei Krisztian Horvath Krisztián Horváth Krisztián Horváth

Publication Name: Machines

Publication Date: 2025-12-01

Volume: 13

Issue: 12

Page Range: Unknown

Description:

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization.

Open Access: Yes

DOI: 10.3390/machines13121141

Image-Based Estimation of Porosity and Tortuosity in Fibrous Acoustic Absorbers

Krisztian Horvath Attila Schweighardt Krisztián Horváth Krisztián Horváth

Publication Name: Engineering Reports

Publication Date: 2025-12-01

Volume: 7

Issue: 12

Page Range: Unknown

Description:

This study presents a fast and non-destructive image-based method for estimating two key acoustic parameters—open porosity and tortuosity—in fibrous sound-absorbing materials. The approach uses a single grayscale optical micrograph, which is down-sampled, contrast-equalized, and segmented via adaptive thresholding. From the resulting binary fiber mask, two geometric descriptors are extracted: coverage and a one-pixel-wide skeleton. Porosity is estimated using a simple linear formula calibrated on three reference materials, yielding an average absolute error below 0.3% when compared with argon gas pycnometry. Tortuosity is inferred from the total skeleton length relative to the image area, producing a stable ranking across materials with consistent bias relative to measured data. Additionally, a random forest model using only three image features—coverage, median fiber radius, and skeleton length—predicts airflow resistivity with over 70% explained variance. The full analysis pipeline is implemented in Python using open-source libraries (OpenCV, scikit-image) and runs in under half a second per image on standard hardware. This makes the method well suited for early-stage material screening, in-line quality control, or laboratory support, without the need for destructive testing or costly instruments. The approach bridges the gap between optical imaging and physical parameter estimation, offering a lightweight alternative to traditional porosity and impedance-tube measurements.

Open Access: Yes

DOI: 10.1002/eng2.70537

Application of Psychoacoustic Metrics in the Noise Assessment of Geared Drives

Krisztian Horvath Krisztián Horváth Krisztián Horváth

Publication Name: World Electric Vehicle Journal

Publication Date: 2025-11-01

Volume: 16

Issue: 11

Page Range: Unknown

Description:

Psychoacoustic metrics offer a valuable complement to traditional noise evaluation methods for gear transmissions, as they account for the human perception of sound quality rather than relying solely on physical measurements. While parameters such as overall sound pressure level (SPL) and spectral content quantify noise intensity and frequency distribution, they often fail to reflect subjective annoyance caused by tonal or high-frequency components common in gear systems. This review provides a structured overview of how psychoacoustic metrics—including loudness, sharpness, roughness, fluctuation strength, and tonality—are applied in the analysis of gear transmission noise. Relevant studies were identified through a comprehensive search across multiple scientific databases, with 54 meeting the inclusion criteria. The findings highlight both the benefits and limitations of these metrics, and present examples of their industrial application in automotive and mechanical engineering contexts. The review also identifies gaps in current research, particularly in integrating psychoacoustic evaluation with predictive modelling and machine learning, and suggests directions for future work.

Open Access: Yes

DOI: 10.3390/wevj16110611

Data-Driven Identification of Gearbox Housing Structures Using Acoustic Radiation Spectra

Krisztian Horvath Krisztián Horváth Krisztián Horváth

Publication Name: International Journal of Basic and Applied Sciences

Publication Date: 2025-08-01

Volume: 14

Issue: 4

Page Range: 619-623

Description:

The structural design of gearbox housing, such as ribbing and wall thickness, has a significant impact on its noise radiation characteristics, especially in electric vehicle applications where tonal noise is more perceptible. This study presents a novel methodology that uses machine learning and spectral analysis to distinguish between gearbox housing types based solely on their acoustic radiation data. Frequency-domain sound pressure spectra, simulated for multiple design variants, were interpolated and analyzed using Principal Component Analysis (PCA) and K-means clustering. The results reveal that construction types (e.g., fully ribbed, partially ribbed, or without ribs) exhibit distinct acoustic profiles. Furthermore, a Random Forest classifier achieved 88.9% accuracy in predicting structural configuration from the spectra alone. These findings demonstrate that structural design features can be inferred directly from acoustic data, offering a lightweight and geometry-free alternative to traditional NVH simulation workflows. The approach can be integrated as a lightweight plug-in in existing NVH workflows. It ingests acoustic spectra and returns a structural-stiffness label with uncertainty, supporting early-stage screening and late-phase regression checks.

Open Access: Yes

DOI: 10.14419/mnbhp030

Multibody Simulation of Helical Gear Noise and Vibration Behavior Using MSC ADAMS †

Ambrus Zelei Krisztian Horvath Krisztián Horváth Krisztián Horváth

Publication Name: Engineering Proceedings

Publication Date: 2025-01-01

Volume: 113

Issue: 1

Page Range: Unknown

Description:

The premium electric-vehicle market demands exceptionally quiet transmissions because the absence of engine masking makes gearbox noise more perceptible. Virtual NVH (noise, vibration, and harshness) evaluation requires coupling elastic deformation, gear–tooth contact, and vibration transmission through bearings and housing within a single environment. This study develops an integrated workflow in MSC ADAMS for predicting the NVH behavior of a 23/81-tooth helical gear pair. Finite element-based flank stiffness is imported, and a nonlinear contact model is applied to flexible teeth. Baseline simulation at 50 Nm and 200 rpm yields a static transmission error (TE) of 7.5 µm and a dynamic peak-to-peak TE of 0.7 µm, with the fundamental mesh tone at 77 Hz. Increasing tip relief by +0.10 mm lowers RMS TE by 31% and the first mesh order by 3.1 dB while raising the flank pressure from 1.65 GPa to 1.88 GPa. The workflow efficiently supports early-stage gear-noise optimization prior to the development of physical prototypes.

Open Access: Yes

DOI: 10.3390/engproc2025113036

Predicting Gear Noise Levels in Electric Multiple Units Based on Microgeometry Modifications Using Clustering and Inverse Distance Weighting †

Ambrus Zelei Krisztian Horvath Krisztián Horváth Krisztián Horváth

Publication Name: Engineering Proceedings

Publication Date: 2025-01-01

Volume: 113

Issue: 1

Page Range: Unknown

Description:

Reducing noise in electric multiple-unit (EMU) gearboxes demands prediction tools that are both rapid and reliable. Gear sound pressure levels vary sharply with micrometre-scale changes such as tooth repair, inclination, or profile relief, yet traditional estimates depend on hours-long CAE simulations. We present a data-driven hybrid surrogate that combines k-means clustering and inverse distance weighting (CLS-IDW) within the ODYSSEE A-Eye platform to map geometry modifications directly to broadband noise. Trained on the open 200-case Romax dataset, the model returns predictions within milliseconds and reproduces unseen operating points, with R2 = 0.75 and a mean absolute error of 2.33 dB, matching solver repeatability. Sensitivity analysis identifies a −7° tooth inclination coupled with a 10 µm repair depth as the most effective combination, lowering noise by 3–5 dB. Eliminating costly CAE loops, the surrogate supports acoustics-aware optimisation at the concept stage, compressing development cycles and enhancing passenger comfort while maintaining transparency for regulatory review.

Open Access: Yes

DOI: 10.3390/engproc2025113034

Population-Level Assessment of Circumferential Flank Waviness Variability Using a ΔW1 Indicator Derived from CMM Measurements

Krisztián Horváth

Publication Name: Applied Sciences Switzerland

Publication Date: 2026-03-01

Volume: 16

Issue: 6

Page Range: Unknown

Description:

Long-wavelength flank waviness plays a critical role in the excitation behavior of geared transmissions. While coordinate measuring machine (CMM) exports provide detailed geometric information, conventional evaluations typically focus on individual tooth curves and do not quantify circumferential inhomogeneity across teeth. This study introduces a tooth-to-tooth long-wavelength waviness inhomogeneity indicator (ΔW1) derived directly from Klingelnberg-style MKA plot files and demonstrates its behavior on a large industrial dataset comprising 3375 measured gear parts. Each flank curve was detrended using a second-order polynomial fit, and lobe-based waviness amplitudes (W1–W3) were extracted via sine–cosine projection. The proposed ΔW1 metric was defined as the difference between the maximum and minimum W1 values across measured teeth within the same part. To eliminate measurement edge effects, a mid-section evaluation (10–90% of the face width) was additionally performed. Population-level analysis revealed consistent separation between geometrically homogeneous and inhomogeneous parts, with ΔW1 values in the most critical components exceeding 7–9 µm after mid-section filtering. Unsupervised clustering based on ΔW1 and maximum W1 further distinguished a high-variability subset of parts exhibiting systematic long-wavelength modulation patterns. The results demonstrate that circumferential waviness variability can be quantified using standard CMM outputs without additional hardware or specialized measurement procedures. The proposed indicator provides a practical geometric screening tool for large production batches and establishes a reproducible framework for linking detailed flank geometry to manufacturing consistency assessment. Although acoustic validation is outside the scope of the present work, the metric is intended as an NVH-relevant geometric risk indicator for future vibroacoustic correlation studies.

Open Access: Yes

DOI: 10.3390/app16063037

Frequency-Band Acoustic Feature Dataset for Comparative Analysis of Electric Vehicle Gearbox Housing Stiffness

Krisztián Horváth

Publication Name: Data

Publication Date: 2026-03-01

Volume: 11

Issue: 3

Page Range: Unknown

Description:

This data descriptor presents a compact acoustic feature dataset derived from an open simulation-based study on electric vehicle gearbox housings with different structural stiffness levels. The dataset contains band-averaged sound pressure level (SPL) features extracted from radiated noise spectra of three housing concepts—flexible, intermediate, and rigid—differing only in ribbing configuration. Frequency-domain SPL spectra in the 1–6 kHz range were partitioned into five one-kilohertz bands, yielding a five-dimensional acoustic feature vector for each housing–microphone combination. In total, twelve feature vectors are provided, accompanied by stiffness labels and metadata describing the underlying simulation context. In addition to the dataset itself, baseline exploratory analyses are reported to illustrate potential reuse scenarios. Principal component analysis and unsupervised clustering demonstrate that mid-frequency bands, particularly between 2 and 4 kHz, exhibit sensitivity to housing stiffness, whereas total integrated spectral energy shows limited discriminative power. These analyses are intended to be illustrative examples rather than predictive models, given the deliberately small dataset size. The dataset is designed for reuse in benchmarking dimensionality reduction methods, clustering algorithms, uncertainty-aware classifications, and educational demonstrations of small-sample NVH data analysis. By providing a transparent and lightweight acoustic feature representation, this contribution supports reproducible research and early-stage comparative studies in drivetrain noise and vibration analysis.

Open Access: Yes

DOI: 10.3390/data11030050

Frequency-Band Sensitivity Mapping of Gearbox Housing Concepts Based on Sound Pressure Spectra

Dániel Feszty Krisztián Horváth

Publication Name: Applied Sciences Switzerland

Publication Date: 2026-03-01

Volume: 16

Issue: 6

Page Range: Unknown

Description:

Gearbox housing stiffness strongly influences radiated noise in electric drivetrains, particularly in the absence of engine masking. While high-fidelity vibro-acoustic simulations provide detailed insight, they are computationally demanding for early-stage design screening. This study investigates whether extremely compact spectral descriptors can encode stiffness-related information. The descriptors consist of five 1 kHz band-averaged sound pressure levels between 1 and 6 kHz. These band-averaged quantities are treated as compact spectral descriptors representing the acoustic response of each gearbox housing configuration. The analysis is based on a simulation-derived dataset of twelve spectra representing three ribbing configurations of a single gearbox housing geometry. A Random Forest classifier evaluated using leave-one-out cross-validation (LOOCV) achieved 0.75 accuracy. Confusion matrix analysis indicates clear separation of the flexible concept. Intermediate and rigid configurations show partial spectral overlap. Permutation testing suggests that the observed classification performance exceeds random chance, although uncertainty remains substantial due to the small dataset size. Feature-importance analysis identifies the 2–4 kHz region as the most stiffness-sensitive frequency range, supporting physical interpretations of mid-frequency structural–acoustic coupling. This exploratory study highlights both the potential and the statistical limits of minimal frequency-band descriptors for rapid NVH stiffness screening under small-sample conditions.

Open Access: Yes

DOI: 10.3390/app16063079

Sensitivity analysis of tooth microgeometric modifications on vibroacoustic behaviour in helical gears with harmonically distributed variations

Ambrus Zelei Krisztián Horváth

Publication Name: Journal of Physics Conference Series

Publication Date: 2026-01-01

Volume: 3190

Issue: 1

Page Range: Unknown

Description:

The virtual acoustic testing of gear drives has gained growing importance. Fundamentally, the transmission error (TE) is predicted, since it directly affects the vibration and noise characteristics. In this study, the effect of the harmonically distributed tooth microgeometric variations on the TE is investigated via elastic multibody simulations of a helical gear pair. The sensitivity analysis focused on the tip relief, the root relief and the barrelling. The model considered different degrees of linear-, quadratic- and cubic-harmonically distributed variations. The results showed that barrelling with quadratic harmonic micro-geometry had the most significant effect on TE. In addition, if harmonic distributions accidentally coincide in the pairing of the gears, the TE is significantly amplified, leading to a pronounced excitation of the system dynamics.

Open Access: Yes

DOI: 10.1088/1742-6596/3190/1/012005

Multiphysics Modeling and Simulation of NVH Phenomena in Electric Vehicle Powertrains

Krisztián Horváth

Publication Name: World Electric Vehicle Journal

Publication Date: 2026-04-01

Volume: 17

Issue: 4

Page Range: Unknown

Description:

The rapid electrification of road vehicles has fundamentally reshaped the priorities of noise, vibration, and harshness (NVH) engineering. In the absence of combustion-related broadband masking, tonal and order-related phenomena originating from the electric machine, inverter switching, and high-speed reduction gearing have become clearly perceptible and, in many cases, acoustically dominant. Consequently, drivetrain noise in electric vehicles can no longer be assessed at component level alone; it must be understood as a coupled system response shaped by excitation mechanisms, structural dynamics, transfer paths, radiation efficiency, and ultimately human perception. This review adopts a source-to-perception perspective and consolidates the principal physical mechanisms governing vibro-acoustic behavior in integrated electric drive units. Electromagnetic force harmonics and torque ripple are discussed alongside transmission-error-driven gear mesh excitation, while bearing and shaft nonlinearities are examined in the context of high-speed operation. In addition, ancillary thermoacoustic and aerodynamic contributions are considered, reflecting the increasingly integrated packaging of modern e-axle architectures. On this mechanism-oriented basis, dominant excitation types are linked to frequency-appropriate modeling strategies, spanning electromagnetic force extraction, multibody drivetrain simulation, structural finite element analysis, transfer path analysis, and acoustic radiation prediction. Particular attention is given to workflow integration across domains. Finally, the paper identifies research challenges that predominantly arise at system level, including multi-source interaction effects, installation-dependent transfer-path variability, emergent resonances in assembled structures, manufacturing-induced tonal artifacts, and the still limited correlation between predicted vibration fields and perceived sound quality.

Open Access: Yes

DOI: 10.3390/wevj17040183

Sequential model predictive direct speed control of PMSM

Karol Kyslan Krisztian Horvath Lukáš Pancurák Krisztián Horváth

Publication Name: Scientific Reports

Publication Date: 2026-12-01

Volume: 16

Issue: 1

Page Range: Unknown

Description:

Finite control set model predictive control (FCS-MPC) has emerged as a powerful strategy for permanent magnet synchronous motor (PMSM) drives. However, its performance strongly depends on appropriately chosen weighting factors, which directly affect control quality and, in some cases, may even lead to instability. Despite the crucial role of weighting factors, there is no systematic or generally accepted procedure for selecting their values, which limits the robustness and practical applicability of conventional FCS-MPC methods. To overcome this limitation, this paper presents the experimental validation of a sequential direct speed predictive control strategy for PMSM. The individual cost functions are evaluated sequentially, thereby tuning is simplified and weighting factors are reduced. Experimental results show that the original version of sequential direct speed control, as proposed in the literature, exhibits promising dynamic performance but suffers from instability and current ripples under certain conditions. To address these issues, an enhanced version of the sequential direct speed predictive control is proposed in the paper. It effectively suppresses instabilities and enhances the speed dynamic response of the drive. The proposed approach was experimentally validated using the OP 5600 rapid control prototyping platform running RT-LAB software and a 1.1 kW PMSM machine.

Open Access: Yes

DOI: 10.1038/s41598-026-39256-2

Curated Vibration Features and an Interpretable Gearbox Health Index (GHI) Baseline for Condition Monitoring Bench-Marking

Krisztian Horvath Krisztián Horváth

Publication Name: Data

Publication Date: 2026-04-01

Volume: 11

Issue: 4

Page Range: Unknown

Description:

This data descriptor provides a standardized and reproducible subsystem-level representation of the NREL wind turbine gearbox condition monitoring benchmarking dataset. The released records are derived from Healthy (H1–H10) and Damaged (D1–D10) measurement files and include subsystem-level standardized indices (KHI_HS, KHI_IMS, KHI_PL) together with a calibrated 0–1 Gearbox Health Index (GHI). The indices are generated using a fully specified and deterministic feature extraction and aggregation workflow based on established vibration indicators and healthy-referenced normalization. The Zenodo deposit contains machine-readable CSV tables intended to support transparent benchmarking across supervised classification and anomaly detection studies. The proposed GHI is introduced as an interpretable and reproducible reference baseline rather than an optimized diagnostic model. Technical validation demonstrates condition-level separability within the analyzed dataset while emphasizing the descriptive nature of the index. By releasing structured derived records and a documented regeneration procedure, this work enables an implementation-independent comparison of gearbox condition monitoring approaches and supports reproducible evaluation of alternative health index formulations. Dataset: 10.5281/zenodo.18832721. Dataset License: Creative Commons Attribution 4.0 International (CC-BY 4.0)

Open Access: Yes

DOI: 10.3390/data11040070

Bridging Diagnostic Condition Monitoring and NVH Tonal Excitation Through Frequency–Domain Structural Mapping

Krisztian Horvath Krisztián Horváth

Publication Name: Applied Sciences Switzerland

Publication Date: 2026-04-01

Volume: 16

Issue: 8

Page Range: Unknown

Description:

Featured Application: The mapping methodology presented in this manuscript can aid in the vibration-based assessment of tonal excitation-related response in powertrain systems, providing a structural link between diagnostic monitoring and NVH assessment practices. In general, condition monitoring (CM) and noise, vibration and harshness (NVH) are often treated as separate disciplines, despite the fact that both rely on vibration measurements. CM relies on broadband statistical metrics such as RMS, kurtosis, and envelope analysis to detect faults. Meanwhile, NVH investigates tonal excitation mechanisms related to gear mesh frequency (GMF) and its modulation components. In this study, we investigate whether a numerical relationship can be established between classical CM indicators and physically based tonal excitation indicators derived from frequency–domain analysis. Using healthy and damaged benchmark gearbox recordings, Spearman correlation analysis was performed between broadband metrics and GMF-related tonal features, including GMF-band energy and absolute sideband energy. Results show moderate but statistically significant correlations between RMS, envelope peak amplitude, and tonal indicators, whereas kurtosis exhibits no meaningful association. Additionally, tonal response amplification in the damaged gearbox is shown to be non-uniformly distributed across sensor locations, indicating sensor-dependent structural sensitivity rather than uniform response growth. These findings demonstrate that broadband CM indicators partially encode changes in tonal excitation-related response, establishing a reproducible data-driven bridge between diagnostic condition monitoring and NVH excitation analysis.

Open Access: Yes

DOI: 10.3390/app16083709