The 4th International Conference on New Materials, Machinery and Vehicle Engineering

2025-07-02

The 4^th International Conference on New Materials, Machinery and Vehicle Engineering

July 11-13. 2025, Changsha, China

The 4^th International Conference on New Materials, Machinery, and Vehicle Engineering (NMMVE 2025) will be held on July 11-13, 2025, in Changsha, P.R. China. The conference aims to gather scientists, researchers and scholars to exchange and share expertise and research results in various aspects of new materials, mechanical and vehicle engineering, and provide an important interdisciplinary platform for researchers, practitioners and educators to showcase the latest trends, challenges and feasible solutions in the field of production, learning and research.

NMMVE is now calling for papers in the following areas of interest:

Structural Materials
Composite Materials
Insulating Material
Environmental Protection Materials
Application of Laser in Material Processing
Compound Material
Polymer Materials
Semiconductor Material
New Functional Materials
Optical / Electronic / Magnetic Materials
Optical Information Materials and Devices
Intelligent Material System
Nano Materials and Technology
New Material Technology
New Energy Materials and Devices
Optical Functional Materials and Devices
Metal Material Engineering
Inorganic Nonmetallic Materials
Materials Science and Engineering
Intelligent Traffic Systems
Machine Design
Optoelectronic Machinery
Robotics and Mechanics
Intelligent Sensing and Recognition Technology
Intelligent Sensors and Sensing Systems
Sensor Technology Nano Friction
Mechanical Dynamics and Vibration
Instruments and Apparatuses
Welding Technology and Equipment
Power Mechanical Engineering
Structure and Design of Spacecraft
Micro Nano Fabrication and Ultra Precision Sensing
Mechanical Manufacturing and its Automation Guidance and Control Bionic Intelligent Cluster Technology
Shaftless Propulsion System
Intelligent Unmanned Aerial VehicleSuper Loop Train Technology
Optical Measurement Equipment and Vehicle Engineering
Micro Electro Mechanical System
Additive Manufacturing
Marine Transportation Equipment
Automatic Driving
Internet of VehiclesTraffic Big Data
Photoelectric Sensors for Traffic Applications
Optoelectronic Engineering and Vehicle Equipment
Application of Photoelectric Detection Technology in Mechanical Design and Manufacturing
Aerospace Engineering
Rail Transit
New Energy Vehicles
Advanced Transportation Safety Systems
AI, Machine Learning and Deep Learning for ITS
Behavioral Modeling in ITS

CONTACT Dr. Liang +86 13107314195 E-mail: nmmve_paper@163.com

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Editor-in-Chief

Prof. Jun Yang

Institute of Acoustics, Chinese Academy of Sciences, China

ISSN

1541-0161 (Print)

2693-1443 (Online)

Publication Frequency

Bi-monthly

Indexing

Web of Science Coverage

Emerging Sources Citation Index (IF 4.2, Q1)

Elsevier Solutions

Scopus (2025 CiteScore 2.0);

Portico, etc.

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

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Member of ASC

Volume Arrangement

Featured Articles

New scaling of critical damping and reduced frequency for mechanically excited systems

This paper introduces a universal framework for understanding the vibration responses of systems subjected to harmonic excitation. By examining a simplified cylinder-spring-damper model, the study refurbishes traditional scaling methods for the excitation frequency ratio and critical damping ratio. The findings indicate that in damped systems, the maximum amplitude of vibration does not align with the natural frequency. This observation leads to the introduction of a new scaling method for reduced frequency. This new approach aligns resonance peaks at the new reduced velocity of 1.0 across different damping ratios, providing a consistent characterization of vibration behavior. A new critical damping ratio of 0.707 is identified for an excited system as opposed to the traditional damping ratio of 1.0 for an unexcited system. Key properties such as maximum amplitude, phase lag, bandwidth, and quality factor are analyzed, demonstrating that the proposed reduced frequency and critical damping ratio effectively capture the dynamics of both damped and undamped excited systems. The findings offer significant insights for practical applications in engineering and various scientific fields.

Ultrasonic wave velocity as a universal metric for defect detection in timber structures: A case study on Japanese cedar wood (Cryptomeria japonica)

This study makes significant contributions to the field of ultrasonic testing (UT) by offering a novel approach to the identification of artificially introduced defects within Japanese cedar wood (Cryptomeria japonica). The findings are of particular relevance for the heritage conservation and construction sectors, where non-invasive defect detection is paramount. The study establishes a robust framework for assessing the structural integrity of timber by correlating ultrasonic wave velocity reductions with defect size and distribution. Big-sized defects led to more substantial decreases in wave velocity. The study establishes a robust framework for assessing the structural integrity of historical timber by correlating ultrasonic wave velocity reductions with defect size and distribution. This framework has the potential to be applicable to diverse wood species and defect types.

Vehicle structural road noise prediction based on an improved Long Short-Term Memory method

The control of vehicle interior noise has become a critical metric for assessing noise, vibration, and harshness (NVH) in vehicles. During the initial phases of vehicle development, accurately predicting the impact of road noise on interior noise is essential for reducing noise levels and expediting the product development cycle. In recent years, data-driven methods based on machine learning have gained significant attention due to their robust capability in navigating complex data mapping relationships. Notably, surrogate models have demonstrated exceptional performance in this domain. Numerous researchers have integrated diverse intelligent algorithms into the study of vehicle noise, leveraging advantages such as the elimination of precise modeling requirements, extensive solution space exploration, continuous learning from data, and robust algorithmic versatility. However, in NVH engineering applications, data-driven models face inherent limitations, particularly in interpretability and stability. To address these issues, this paper introduces an improved Long Short-Term Memory (LSTM) network that combines knowledge and data. Inspired by the physical information neural network concept, this approach incorporates values calculated through empirical formulas into the neural network as constraints. Comparative assessments with traditional LSTM networks highlight the advantages of this deep learning model. By integrating empirical formulas constraints, the model not only enhances interpretability but also achieves robust generalization with fewer data samples. The proposed method is validated on a specific vehicle model, showing significant improvements in prediction accuracy and efficiency.