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Applied Sciences
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Machine Learning for Noise and Vibration Engineering
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Machine Learning for Noise and Vibration Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 10868

Special Issue Editors

Dr. Christian Adams

E-Mail Website
Guest Editor
Department of Mechanical Engineering, System Reliability, Adaptive Structures, and Machine Acoustics SAM, Technical University of Darmstadt, 64287 Darmstadt, Germany
Interests: design for acoustics; modeling, machine learning, and similitude in vibroacoustics; condition monitoring
Dr. Merten Stender

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Hamburg University of Technology, 21073 Hamburg, Germany
Interests: complex dynamics; digital twins; physics-informed learning; friction
Prof. Dr. Tyler Dare

E-Mail Website
Guest Editor
Graduate Program in Acoustics, The Pennsylvania State University, University Park, PA 16802, USA
Interests: structural acoustics; experimental vibroacoustics; signal analysis

Special Issue Information

Dear Colleagues,

Machine learning techniques offer new pathways in noise and vibration engineering. New modeling techniques emerge that make use of large data sets, pattern recognition deepens the insight into noise and vibration phenomena, and new algorithms can “learn” to identify the dynamic properties of mechanical structures. The Special Issue “Machine Learning for Noise and Vibration Engineering” aims at reporting on machine learning techniques in noise and vibration engineering. Authors are invited to submit their original work, including but not limited to:

  • Artificial neural networks, deep learning including statistical analyses and explainability techniques;
  • Data-driven vibration and acoustic modeling techniques;
  • Inverse problems in vibration and acoustics based on machine learning;
  • engineering noise control by means of data-driven approaches;
  • Pattern recognition and pattern prediction including applications in vibration and acoustic-based condition monitoring;
  • Identifying, quantifying, and controlling uncertainty of data-driven models.

Dr. Christian Adams
Dr. Merten Stender
Prof. Dr. Tyler Dare
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mechanical vibrations
  • acoustics
  • machine learning
  • data-driven modeling
  • explainable machine learning
  • pattern recognition
  • engineering noise control
  • condition monitoring
  • open science

Published Papers (5 papers)

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Research

18 pages, 2009 KiB  
Article
On the Relationship of the Acoustic Properties and the Microscale Geometry of Generic Porous Absorbers
by Tobias P. Ring and Sabine C. Langer
Appl. Sci. 2022, 12(21), 11066; https://doi.org/10.3390/app122111066 - 1 Nov 2022
Cited by 1 | Viewed by 1338
Abstract
When tailoring porous absorbers in acoustic applications, an appropriate acoustic material model, as well as the relationship between the material model parameters and the microscale geometry of the material, is indispensable. This relationship can be evaluated analytically only for few simple material geometries. [...] Read more.
When tailoring porous absorbers in acoustic applications, an appropriate acoustic material model, as well as the relationship between the material model parameters and the microscale geometry of the material, is indispensable. This relationship can be evaluated analytically only for few simple material geometries. Machine-learning models can close this gap for complex materials, but due to their black-box nature, the interpretability of obtained inferences is rather low. Therefore, an existing neural network model that predicts the acoustic properties of a porous material based on the microscale geometry is subject to statistics-based sensitivity analysis. This is conducted to gain insights into the relationship between the microscale geometry and the acoustic material parameters of a generic bar-lattice design porous material. Although it is a common approach in the field of explainable artificial intelligence research, this has not been widely investigated for porous materials yet. By deriving statistics-based sensitivity measures from the neural network model, the explainability and interpretability is increased and insights into the relationship of the acoustic properties and their microscale geometry of the porous specimen can be obtained. The results appear plausible and comparable to existing studies available in the literature, showing if and how the bar-lattice geometry influences the acoustic material parameters. Moreover, it could be shown that the applied global sensitivity analysis method allows us to not only derive a one-to-one parameter impact relation, but also reveals interdependencies that are important to address during a material tailoring process. Full article
(This article belongs to the Special Issue Machine Learning for Noise and Vibration Engineering)
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21 pages, 1802 KiB  
Article
On Machine-Learning-Driven Surrogates for Sound Transmission Loss Simulations
by Barbara Zaparoli Cunha, Abdel-Malek Zine, Mohamed Ichchou, Christophe Droz and Stéphane Foulard
Appl. Sci. 2022, 12(21), 10727; https://doi.org/10.3390/app122110727 - 23 Oct 2022
Cited by 2 | Viewed by 1747
Abstract
Surrogate models are data-based approximations of computationally expensive simulations that enable efficient exploration of the model’s design space and informed decision making in many physical domains. The usage of surrogate models in the vibroacoustic domain, however, is challenging due to the non-smooth, complex [...] Read more.
Surrogate models are data-based approximations of computationally expensive simulations that enable efficient exploration of the model’s design space and informed decision making in many physical domains. The usage of surrogate models in the vibroacoustic domain, however, is challenging due to the non-smooth, complex behavior of wave phenomena. This paper investigates four machine learning (ML) approaches in the modelling of surrogates of sound transmission loss (STL). Feature importance and feature engineering are used to improve the models’ accuracy while increasing their interpretability and physical consistency. The transfer of the proposed techniques to other problems in the vibroacoustic domain and possible limitations of the models are discussed. Experiments show that neural network surrogates with physics-guided features have better accuracy than other ML models across different STL models. Furthermore, sensitivity analysis methods are used to assess how physically coherent the analyzed surrogates are. Full article
(This article belongs to the Special Issue Machine Learning for Noise and Vibration Engineering)
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23 pages, 557 KiB  
Article
Vehicle Impact on Tire Road Noise and Validation of an Algorithm to Virtually Change Tires
by Michael Leupolz and Frank Gauterin
Appl. Sci. 2022, 12(17), 8810; https://doi.org/10.3390/app12178810 - 1 Sep 2022
Cited by 3 | Viewed by 1624
Abstract
Especially for electric vehicles, the tire impact on car noise is becoming more and more important. The requirement of meeting certification criteria makes estimating the sound pressure level essential for vehicle manufacturers. Most recent research on tire road noise is conducted on component [...] Read more.
Especially for electric vehicles, the tire impact on car noise is becoming more and more important. The requirement of meeting certification criteria makes estimating the sound pressure level essential for vehicle manufacturers. Most recent research on tire road noise is conducted on component test benches. Little research exists into tires mounted on vehicles, and even less into the impact of acceleration on the generated noise. The literature mainly considers some vehicle shape differences, tire load, and inflation pressure. This article investigates the impact of different vehicles on tire noise through a series of measurements on a standardized test track. The rolling noise as well as accelerated noise of three different tires and five different vehicles are compared. The impact of the drive axle on accelerated noise as well as a weight variation is investigated. Additionally to the absolute measured differences between the vehicles, statistical methods are used to separate measurement dispersion from actual systematic differences. This research therefore validates older research on the vehicles’ impact on tire noise, which is necessary since the general tire structure, thread, and rubber composition have changed in the time period between the publication of previous research from the literature and this paper. This allows us to approximate the emitted noise on different vehicles. Furthermore, we validate an algorithm to virtually change tires on test benches. The algorithm is standardized and implemented in common measurement software. Full article
(This article belongs to the Special Issue Machine Learning for Noise and Vibration Engineering)
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21 pages, 6868 KiB  
Article
Uncertainty Analysis and Experimental Validation of Identifying the Governing Equation of an Oscillator Using Sparse Regression
by Yaxiong Ren, Christian Adams and Tobias Melz
Appl. Sci. 2022, 12(2), 747; https://doi.org/10.3390/app12020747 - 12 Jan 2022
Cited by 4 | Viewed by 1613
Abstract
In recent years, the rapid growth of computing technology has enabled identifying mathematical models for vibration systems using measurement data instead of domain knowledge. Within this category, the method Sparse Identification of Nonlinear Dynamical Systems (SINDy) shows potential for interpretable identification. Therefore, in [...] Read more.
In recent years, the rapid growth of computing technology has enabled identifying mathematical models for vibration systems using measurement data instead of domain knowledge. Within this category, the method Sparse Identification of Nonlinear Dynamical Systems (SINDy) shows potential for interpretable identification. Therefore, in this work, a procedure of system identification based on the SINDy framework is developed and validated on a single-mass oscillator. To estimate the parameters in the SINDy model, two sparse regression methods are discussed. Compared with the Least Squares method with Sequential Threshold (LSST), which is the original estimation method from SINDy, the Least Squares method Post-LASSO (LSPL) shows better performance in numerical Monte Carlo Simulations (MCSs) of a single-mass oscillator in terms of sparseness, convergence, identified eigenfrequency, and coefficient of determination. Furthermore, the developed method SINDy-LSPL was successfully implemented with real measurement data of a single-mass oscillator with known theoretical parameters. The identified parameters using a sweep signal as excitation are more consistent and accurate than those identified using impulse excitation. In both cases, there exists a dependency of the identified parameter on the excitation amplitude that should be investigated in further research. Full article
(This article belongs to the Special Issue Machine Learning for Noise and Vibration Engineering)
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19 pages, 894 KiB  
Article
Gaussian-Based Machine Learning Algorithm for the Design and Characterization of a Porous Meta-Material for Acoustic Applications
by Alessandro Casaburo, Dario Magliacano, Giuseppe Petrone, Francesco Franco and Sergio De Rosa
Appl. Sci. 2022, 12(1), 333; https://doi.org/10.3390/app12010333 - 30 Dec 2021
Cited by 16 | Viewed by 2562
Abstract
The scope of this work is to consolidate research dealing with the vibroacoustics of periodic media. This investigation aims at developing and validating tools for the design and characterization of global vibroacoustic treatments based on foam cores with embedded periodic patterns, which allow [...] Read more.
The scope of this work is to consolidate research dealing with the vibroacoustics of periodic media. This investigation aims at developing and validating tools for the design and characterization of global vibroacoustic treatments based on foam cores with embedded periodic patterns, which allow passive control of acoustic paths in layered concepts. Firstly, a numerical test campaign is carried out by considering some perfectly rigid inclusions in a 3D-modeled porous structure; this causes the excitation of additional acoustic modes due to the periodic nature of the meta-core itself. Then, through the use of the Delany–Bazley–Miki equivalent fluid model, some design guidelines are provided in order to predict several possible sets of characteristic parameters (that is unit cell dimension and foam airflow resistivity) that, constrained by the imposition of the total thickness of the acoustic package, may satisfy the target functions (namely, the frequency at which the first Transmission Loss (TL) peak appears, together with its amplitude). Furthermore, when the Johnson–Champoux–Allard model is considered, a characterization task is performed, since the meta-material description is used in order to determine its response in terms of resonance frequency and the TL increase at such a frequency. Results are obtained through the implementation of machine learning algorithms, which may constitute a good basis in order to perform preliminary design considerations that could be interesting for further generalizations. Full article
(This article belongs to the Special Issue Machine Learning for Noise and Vibration Engineering)
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