Krisztián Horváth

60376279400

Publications - 6

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