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Machines
Special Issues
Intelligent Applications in Mechanical Engineering
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Intelligent Applications in Mechanical Engineering

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 3025

Special Issue Editors

Dr. Haibo Huang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: vehicle big data analysis; noise and vibration control; intelligent driving
Special Issues, Collections and Topics in MDPI journals
Dr. Jun Wu

E-Mail Website
Guest Editor
Department of Biology, University of Oxford, South Parks Road, Oxford OX1 3RB, UK
Interests: dynamics and control of vibrations in mechanical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Chongrui Liu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: acoustic metamaterials; noise and vibration control; acoustic–solid coupling

Special Issue Information

Dear Colleagues,

Mechanical engineering is a fundamental discipline in the engineering field, covering multiple areas, such as advanced manufacturing, engineering design, and reliability analysis. The rapid development of artificial intelligence (including deep learning, neural networks, and big data analytics) is driving significant advances in these fields, bringing both new opportunities and new challenges. This Special Issue focuses on the latest research in artificial intelligence-driven applications in mechanical engineering, emphasizing interdisciplinary advances and emerging technologies.

Dr. Haibo Huang
Dr. Jun Wu
Dr. Chongrui Liu
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. Machines is an international peer-reviewed open access monthly 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

  • data-driven applications
  • active and passive control
  • prognostics health management
  • noise, vibration and harshness
  • vehicle road noise analysis
  • vehicle sound package analysis
  • acoustic metamaterials

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

20 pages, 2424 KB  
Article
Predicting Vehicle-Engine-Radiated Noise Based on Bench Test and Machine Learning
by Ruijun Liu, Yingqi Yin, Yuming Peng and Xu Zheng
Machines 2025, 13(8), 724; https://doi.org/10.3390/machines13080724 - 15 Aug 2025
Viewed by 261
Abstract
As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency [...] Read more.
As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization. Full article
(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)
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12 pages, 2871 KB  
Article
Multi-Objective Optimization Design of Low-Frequency Band Gap for Local Resonance Acoustic Metamaterials Based on Genetic Algorithm
by Jianjiao Deng, Yunuo Qin, Xi Chen, Yanyong He, Yu Song, Xinpeng Zhang, Wenting Ma, Shoukui Li and Yudong Wu
Machines 2025, 13(7), 610; https://doi.org/10.3390/machines13070610 - 16 Jul 2025
Viewed by 376
Abstract
Driven by the urgent demand for low-frequency vibration and noise control in engineering scenarios such as automobiles, acoustic metamaterials (AMs), as a new class of functional materials, have demonstrated significant application potential. This paper proposes a low-frequency band gap optimization design method for [...] Read more.
Driven by the urgent demand for low-frequency vibration and noise control in engineering scenarios such as automobiles, acoustic metamaterials (AMs), as a new class of functional materials, have demonstrated significant application potential. This paper proposes a low-frequency band gap optimization design method for local resonance acoustic metamaterials (LRAMs) based on a multi-objective genetic algorithm. Within a COMSOL Multiphysics 6.2 with MATLAB R2024b co-simulation framework, a parameterized unit cell model of the metamaterial is constructed. The optimization process targets two objectives: minimizing the band gap’s deviation from the target and reducing the structural mass. A multi-objective fitness function is formulated by incorporating the band gap deviation and structural mass constraints, and non-dominated sorting genetic algorithm II (NSGA-II) is employed to perform a global search over the geometric parameters of the resonant unit. The resulting Pareto-optimal solution set achieves a unit cell mass as low as 26.49 g under the constraint that the band gap deviation does not exceed 2 Hz. The results of experimental validation show that the optimized metamaterial configuration reduces the peak of the low-frequency frequency response function (FRF) at 63 Hz by up to 75% in a car door structure. Furthermore, the simulation predictions exhibit good agreement with the experimental measurements, confirming the effectiveness and reliability of the proposed method in engineering applications. The proposed multi-objective optimization framework is highly general and extensible and capable of effectively balancing between the acoustic performance and structural mass, thus providing an efficient engineering solution for low-frequency noise control problems. Full article
(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)
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21 pages, 3178 KB  
Article
The Prediction of Sound Insulation for the Front Wall of Pure Electric Vehicles Based on AFWL-CNN
by Yan Ma, Jie Yan, Jianjiao Deng, Xiaona Liu, Dianlong Pan, Jingjing Wang and Ping Liu
Machines 2025, 13(6), 527; https://doi.org/10.3390/machines13060527 - 17 Jun 2025
Viewed by 294
Abstract
The front wall acoustic package system plays a crucial role in automotive design, and its performance directly affects the quality and comfort of the interior noise. In response to the limitations of traditional experimental and simulation methods in terms of accuracy and efficiency, [...] Read more.
The front wall acoustic package system plays a crucial role in automotive design, and its performance directly affects the quality and comfort of the interior noise. In response to the limitations of traditional experimental and simulation methods in terms of accuracy and efficiency, this paper proposes a convolutional neural network (AFWL-CNN) based on adaptive weighted feature learning. Using a data-driven method, the sound insulation performance of the entire vehicle’s front wall acoustic package system was predicted and analyzed based on the original parameters of the front wall acoustic package components, thereby effectively avoiding the shortcomings of traditional TPA and CAE methods. Compared to the traditional CNN model (RMSE = 0.042, MAE = 3.89 dB, I-TIME = 13.67 s), the RMSE of the proposed AFWL-CNN model was optimized to 0.031 (approximately 26.19% improvement), the mean absolute error (MAE) was reduced to 2.84 dB (approximately 26.99% improvement), and the inference time (I-TIME) increased to 17.16 s (approximately 25.53% increase). Although the inference time of the AFWL-CNN model increased by 25.53% compared to the CNN model, it achieved a more significant improvement in prediction accuracy, demonstrating a reasonable trade-off between efficiency and accuracy. Compared to AFWL-LSTM (RMSE = 0.039, MAE = 3.35 dB, I-TIME = 19.81 s), LSTM (RMSE = 0.044, MAE = 4.07 dB, I-TIME = 16.71 s), and CNN–Transformer (RMSE = 0.040, MAE = 3.74 dB, I-TIME = 19.55 s) models, the AFWL-CNN model demonstrated the highest prediction accuracy among the five models. Furthermore, the proposed method was verified using the front wall acoustic package data of a new car model, and the results showed the effectiveness and reliability of this method in predicting the acoustic package performance of the front wall system. This study provides a powerful tool for fast and accurate performance prediction of automotive front acoustic packages, significantly improving design efficiency and providing a data-driven framework that can be used to solve other vehicle noise problems. Full article
(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)
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20 pages, 4089 KB  
Article
Prediction of Vehicle Interior Wind Noise Based on Shape Features Using the WOA-Xception Model
by Yan Ma, Hongwei Yi, Long Ma, Yuwei Deng, Jifeng Wang, Yudong Wu and Yuming Peng
Machines 2025, 13(6), 497; https://doi.org/10.3390/machines13060497 - 6 Jun 2025
Cited by 1 | Viewed by 1209
Abstract
In order to confront the challenge of efficiently evaluating interior wind noise levels in passenger vehicles during the early stages of shape design, this paper proposes a methodology for predicting interior wind noise. The methodology integrates vehicle shape features with a whale optimization [...] Read more.
In order to confront the challenge of efficiently evaluating interior wind noise levels in passenger vehicles during the early stages of shape design, this paper proposes a methodology for predicting interior wind noise. The methodology integrates vehicle shape features with a whale optimization Xception model (WOA-Xception). A nonlinear mapping model is constructed between the vehicle shape features and the wind noise level at the driver’s right ear. This model is constructed using key exterior parameters, which are extracted from wind tunnel test data under typical operating conditions. The exterior parameters include the front windshield, A-pillar, and roof. The key hyperparameters of the Xception model are adaptively optimized using the whale optimization algorithm to improve the prediction accuracy and generalization ability of the model. The prediction results on the test set demonstrate that the WOA-Xception model attains mean absolute percentage error (MAPE) values of 9.78% and 9.46% and root mean square error (RMSE) values of 3.73 and 4.06, respectively, for sedan and Sports Utility Vehicle (SUV) samples, with prediction trends that align with the measured data. A comparative analysis with traditional Xception, WOA-LSTM, and Long Short-Term Memory (LSTM) models further validates the advantages of this model in terms of accuracy and stability, and it still maintains good generalization ability on an independent validation set (mean absolute percentage error of 9.45% and 9.68%, root mean square error of 3.77 and 4.15, respectively). The research findings provide an efficient and feasible technical approach for the rapid assessment of in-vehicle wind noise performance and offer a theoretical basis and engineering references for noise, vibration, and harshness (NVH) optimization design during the early shape phase of vehicle development. Full article
(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)
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20 pages, 5954 KB  
Article
Research on Vehicle Road Noise Prediction Based on AFW-LSTM
by Yan Ma, Ruxue Dai, Tao Liu, Jian Liu, Shukai Yang and Jingjing Wang
Machines 2025, 13(5), 425; https://doi.org/10.3390/machines13050425 - 19 May 2025
Viewed by 600
Abstract
The electrification of automobiles makes low-frequency road noise the main factor affecting the performance of automobile NVH (Noise, Vibration and Harshness). High-precision and high-efficiency road noise prediction results are the basis for NVH performance improvement and optimization. However, using the traditional TPA (transfer [...] Read more.
The electrification of automobiles makes low-frequency road noise the main factor affecting the performance of automobile NVH (Noise, Vibration and Harshness). High-precision and high-efficiency road noise prediction results are the basis for NVH performance improvement and optimization. However, using the traditional TPA (transfer path analysis) method and CAE (Computer-Aided Engineering) method to analyze the road noise problem has the problems of complex transfer path, difficult acquisition of modeling parameters, long duration and high cost. Therefore, based on the road noise hierarchy constructed according to the road noise transmission path, the LSTM (Long Short-Term Memory) network is introduced to establish a data-driven prediction model, which effectively avoids the defects of the TPA method and CAE in analyzing road noise problems. Based on the LSTM prediction model, the AFW (adaptive feature weight) method is introduced to improve the model’s attention to the key features in the input data and finally improve the accuracy and robustness of the road noise prediction model. The results show that the accuracy (RMSE = 1.74 (dB)) and generalization ability (MAE = 2.60 (dB), R2 = 0.924) of the AFW-LSTM model are better than other models. Full article
(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)
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