Vehicle Lightweight Material Design and Manufacturing Technology

A Special Issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 7 November 2026 | Viewed by 823

Editors


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Guest Editor
School of Automotive, Chang'an University, Xi'an, China
Interests: mechanics of cellular materials; machine learning accelerated design of metamaterials

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Guest Editor
School of Automotive, Chang'an University, Xi'an, China
Interests: vibration-assisted plastic forming and deformation mechanisms of metallic materials; advanced forming technologies for lightweight automotive materials and components

E-Mail Website
Guest Editor
School of Automotive, Chang'an University, Xi'an, China
Interests: advanced joining technologies for lightweight and dissimilar automotive materials; AI-enabled and data-driven process optimization and performance prediction

Special Issue Information

Dear Colleagues,

With the rapid development of electric vehicles, automotive lightweight technology—a core breakthrough point for improving driving range, reducing energy consumption and emissions, and enhancing safety—is undergoing a paradigm shift from weight reduction to multi-dimensional coordination of "safety—light weight—functionality". Lightweight technology systems characterized by the integrated collaborative innovation of "material—structure—process" are triggering a profound restructuring of the industry. Frontier directions and innovative technologies such as multiscale simulation methods, intelligent optimization design theories, new multifunctional materials/structures, and advanced manufacturing processes have become research hotspots in the field of electric vehicles.

With the field facing these opportunities and challenges, we invite you to submit your research to the Special Issue of Vehicles “Vehicle Lightweight Material Design and Manufacturing Technology” .

Submissions employing theoretical, experimental, computational, or hybrid methodologies are welcome, and areas of interest include, but are not limited to, the following key topics:

  1. Mechanical Property Characterization and Regulation of Lightweight Automotive Metamaterials/Structures;
  2. Performance-Driven Optimal Design of Lightweight Automotive Materials/Structures;
  3. AI-Enabled Intelligent and Lightweight Design of Multimaterial Vehicle Body Structures;
  4. Digital Twin-Driven Lightweight Design Methods for Vehicles;
  5. Integrated Design and Manufacturing Technology for Lightweight and High-Strength Automotive Components;
  6. Data-Driven Collaborative Design Optimization of Lightweight and Passive Safety;
  7. Multiscale Simulation Technology for Material–Component–Vehicle Performance Sequence;
  8. Additive Manufacturing Technology for Lightweight and Multifunctional Automotive Materials/Structures;
  9. Advanced Joining Technology for Dissimilar Automotive Materials.

Dr. Geng Luo
Dr. De'an Meng
Dr. Liangyu Fei
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vehicles 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 1800 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

  • metamaterials
  • AI
  • manufacturing technology
  • additive manufacturing
  • multi-scale simulation

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Published Papers (1 paper)

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Research

23 pages, 17868 KB  
Article
Machine Learning-Driven Multi-Objective Sizing Optimization, Performance Prediction and Feature Correlation Analysis of Vehicle Frame
by Xianren Zhou, Zhongmin Wang, Guangshuai Xu, Yi Zheng, Deguang Li, Jun Lan, Feiyong Long, Longjie Li, Dianhui Wang, Huarong Liu, Zebing Xu, Chenggang Hao and Yonghua Shi
Vehicles 2026, 8(8), 171; https://doi.org/10.3390/vehicles8080171 - 25 Jul 2026
Viewed by 442
Abstract
To overcome the challenges in conventional frame structure design, namely the difficulty in balancing lightweight design and performance enhancement, the low efficiency of finite element (FE) simulation, and the tedious process of multivariable preliminary screening, an efficient optimization framework for frame structures that [...] Read more.
To overcome the challenges in conventional frame structure design, namely the difficulty in balancing lightweight design and performance enhancement, the low efficiency of finite element (FE) simulation, and the tedious process of multivariable preliminary screening, an efficient optimization framework for frame structures that integrates multi-objective size optimization, machine learning-based performance prediction, and feature correlation analysis is proposed. First, for the steel–aluminum hybrid frame (with the main load-bearing components made of 6005A aluminum alloy and the critical load-bearing supports and joints made of Q345 low-alloy high-strength steel), a trade-off solution is obtained through multi-objective size optimization. Verified by FE simulation, this solution reduces the frame mass by 6.37% and increases the torsional stiffness by 10.47% compared with the initial design. At the same time, the modal performance, structural strength, and deformation control capability are all significantly improved, achieving a precise balance between lightweighting and stiffness enhancement. Second, a linear regression prediction model is constructed to achieve high-accuracy predictions. The average prediction error for torsional stiffness is only 2%, and the maximum prediction error for the seventh-order modal frequency is less than 1%. The prediction time for a single sample is less than one second, which is more than 1000 times faster than conventional FE simulation, thus efficiently replacing time-consuming simulation analyses. Finally, feature correlation analysis is adopted as an alternative to traditional sensitivity analysis. The core variables identified by this method are highly consistent with those obtained from Hypermesh sensitivity analysis, enabling rapid multivariable screening without additional simulations and greatly improving the efficiency of the preliminary analysis phase. The proposed optimization framework achieves a favorable combination of optimization effectiveness, prediction accuracy, and design efficiency. It not only provides a feasible engineering solution for the lightweight design of frame structures but also serves as a technical reference for the efficient optimization of similar complex structures, demonstrating significant engineering application value. Full article
(This article belongs to the Special Issue Vehicle Lightweight Material Design and Manufacturing Technology)
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