Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Browser

Sustainable Engineering: Intelligent Design and New Material Development for Automobiles

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 7282

Share This Special Issue

Special Issue Editors

Dr. Fengxiang Xu

E-Mail Website
Guest Editor

1. Hubei Key Laboratory of Advanced Technology of Automotive Components, Wuhan University of Technology, Wuhan 430070, China
2. Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China
Interests: intelligent design method of automobile body; bionic design of new automobile structure; development of new materials for automobile body

Prof. Dr. Hu Wang

E-Mail Website
Guest Editor

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: Complex product design methods; Fast calculation method; Multi physical field battery simulation and structure design; Parallel computing method reverse method

Dr. Jianguang Fang

E-Mail Website
Guest Editor

School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: constitutive model of concrete; computational mechanics; phase field modelling of brittle and ductile fracture; engineering optimisation; energy absorption; 3D/4D printing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Liming Chen

E-Mail Website
Guest Editor

College of Aerospace Engineering, Chongqing University, Chongqing 400030, China
Interests: mechanics of composite materials and structures; impact damage and failure of structures; mechanics of metamaterials and metastructures; functional integrated design of aerospace materials and structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Driven by an ever-growing demand for resource-conserving and performance-excellent vehicles, lightweight bodies have been a long-term unremitting pursuit in new energy automotive engineering. With the corresponding intelligent design and new materials, technical goals such as lightweight control, high strength and long durability are sustainably achieved. Thus, sustainable engineering based on intelligent optimization and new materials does not merely provide solutions for energy saving and protecting the environment but also improves the performance of the car through specific design theories. Those intelligent design methods could realize the optimal design of expensive black-box functions mainly including multi-objective heuristic algorithms, machine learning algorithms, efficient intelligent sampling methods, multi-objective decision-making methods, new intelligent methods based on few-shot learning, etc. Furthermore, in order to further break through the limitations of automotive performance design, new materials such as composite materials, porous materials and metamaterials can break through the limitations of traditional materials and natural structures and further improve their mechanical properties. Therefore, the current data-driven intelligent optimization methods and new material design can complement and promote each other, and there is a lot of development room for new energy automotive automobiles.

This Special Issue of Sustainability solicits articles that present new concepts and methods as well as case studies in the field of intelligent design and new material development for automobiles. The overall goal is to exchange research and applied working knowledge in the field of automotive structural engineering design.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Multi-objective evolutionary algorithm based on heuristic information
Multi-objective optimization method driven by adaptive learning mechanism
Machine learning optimization method for small sample data
Objective-oriented smart sampling strategy for expensive black-box functions
Optimization and applications of light materials (high strength steel, aluminium alloy and magnesium alloy, etc.)
Research and design application of mechanical properties of fibre reinforced composite materials
Mechanism research and optimization design of porous materials
Mechanism research and optimization design of new mechanical metamaterials

We look forward to receiving your contributions.

Dr. Fengxiang Xu
Prof. Dr. Hu Wang
Dr. Jianguang Fang
Prof. Dr. Liming Chen
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. Sustainability 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

intelligent optimization
multi-objective heuristic algorithm
machine learning algorithm
intelligent sampling method
multi-objective decision-making method
light alloy
composite materials
porous materials
metamaterials

Published Papers (4 papers)

Download All Papers

Research

17 pages, 3809 KiB

Open AccessArticle

Parametric Analysis and Multi-Objective Optimization of Pentamode Metamaterial

by Zhen Zou, Fengxiang Xu, Yuxiong Pan, Xiaoqiang Niu, Tengyuan Fang and Chao Zeng

Sustainability 2023, 15(4), 3421; https://doi.org/10.3390/su15043421 - 13 Feb 2023

Cited by 3 | Viewed by 1234

Abstract

Pentamode metamaterial (PM) has enormous application potential in the design of lightweight bodies with superior vibration and noise-reduction performance. To offer systematic insights into the investigation of PMs, this paper studies the various effects (i.e., unit cell arrangement, material, and geometry) on bandgap properties through the finite element method (FEM). With regards to the influences of unit cell arrangements on bandgap properties, the results show that the PM with triangular cell arrangement (PMT) possesses better bandgap properties than the others. The effects of material and geometry on bandgap properties are then explored thoroughly. In light of the spring-mass system theory, the regulation mechanism of bandgap properties is discussed. Multi-objective optimization is conducted to further enhance the bandgap properties of PMT. Based on the Latin hypercube design and double-points infilling, a high-accuracy Kriging model, which represents the relationship between the phononic bandgap (PBG), single mode phononic bandgap (SPBG), double-cone width, and node radius, is established to seek the Pareto optimal solution sets, using the non-dominated sorting genetic algorithm (NSGA-II). A fitness function is then employed to obtain the final compromise solution. The PBG and total bandgap of PMT are widened approximately 2.2 and 0.27 times, respectively, while the SPBG is narrowed by about 0.51 times. The research offers important understanding for the investigation of PM with superior acoustic regulation capacity. Full article

(This article belongs to the Special Issue Sustainable Engineering: Intelligent Design and New Material Development for Automobiles)

► Show Figures

Figure 1

23 pages, 11445 KiB

Open AccessArticle

A Variable-Fidelity Multi-Objective Evolutionary Method for Polygonal Pin Fin Heat Sink Design

by Xinjian Deng, Enying Li and Hu Wang

Sustainability 2023, 15(2), 1104; https://doi.org/10.3390/su15021104 - 6 Jan 2023

Cited by 1 | Viewed by 949

Abstract

For the multi-objective design of heat sinks, several evolutionary algorithms usually require many iterations to converge, which is computationally expensive. Variable-fidelity multi-objective (VFO) methods were suggested to improve the efficiency of evolutionary algorithms. However, multi-objective problems are seldom optimized using VFO. Therefore, a variable-fidelity evolutionary method (VFMEM) was suggested. Similar to other variable-fidelity algorithms, VFMEM solves a high-fidelity model using a low-fidelity model. Compared with other algorithms, the distinctive characteristic of VFMEM is its application in multi-objective optimization. First, the suggested method uses a low-fidelity model to locate the region where the global optimal solution might be found. Sequentially, both high- and low-fidelity models can be integrated to find the real global optimal solution. Circulation distance elimination (CDE) was suggested to uniformly obtain the PF. To evaluate the feasibility of VFMEM, two classical benchmark functions were tested. Compared with the widely used multi-objective particle swarm optimization (MOPSO), the efficiency of VFMEM was significantly improved and the Pareto frontier (PFs) could also be obtained. To evaluate the algorithm’s feasibility, a polygonal pin fin heat sink (PFHS) design was carried out using VFMEM. Compared with the initial design, the results showed that the mass, base temperature, and temperature difference of the designed optimum heat sink were decreased 5.5%, 18.5%, and 62.0%, respectively. More importantly, if the design was completed directly by MOPSO, the computational cost of the entire optimization procedure would be significantly increased. Full article

(This article belongs to the Special Issue Sustainable Engineering: Intelligent Design and New Material Development for Automobiles)

► Show Figures

Figure 1

18 pages, 5003 KiB

Open AccessArticle

Investigations on the Mechanical Properties of Composite T-Joints with Defects under Bending Loading

by Geng Luo, Chengpeng Chai, Junzhe Liu, Yaozhi Xiao, Yisong Chen and Fengxiang Xu

Sustainability 2022, 14(24), 16609; https://doi.org/10.3390/su142416609 - 12 Dec 2022

Cited by 2 | Viewed by 1320

Abstract

The composite T-joint, a typical structural element, is widely used in the fields of aerospace due to their excellent mechanical properties. However, several defects might occur in this material during manufacturing because of the application of co-curing and co-bonding technologies. The existence of the defects results in the reduction in the load bearing capacity, which negatively affects the safety of the structure. In this study, a finite element (FE) model of composite T-joints is established, which is verified by quasi-static tests to investigate the load bearing capacity and failure modes of composite T-joints under bending loadings with different types of defects, including core material defects, radius floating of the fillet, and debonding defects. It is indicated that the failure modes of T-joints with different kinds of defects under bending loadings are similar, i.e., the delamination occurs firstly at the interface between the filling area and the L-rib before expanding to both sides. Meanwhile, the types of defects exert great effects on the load bearing capacity of T-joints, and the debonding defects in the arc area represent the most dangerous one. Furthermore, the orthogonal test method was adopted to analyze the influence of combined defects on the load bearing capacity of T-joints, and the findings reveal that the most sensitive type of defect is the debonding defects, followed by the radius floating of the fillet, and then core material defects. This result indicates that combined defects have a coupling effect on the load bearing capacity of composite T-joints. This study provides theoretical guidance and technical support for the repair of the defects of composite T-joints. Full article

(This article belongs to the Special Issue Sustainable Engineering: Intelligent Design and New Material Development for Automobiles)

► Show Figures

Figure 1

26 pages, 4440 KiB

Open AccessArticle

A Novel Method of Fault Diagnosis for Injection Molding Systems Based on Improved VGG16 and Machine Vision

by Zhicheng Hu, Zhengjie Yin, Ling Qin and Fengxiang Xu

Sustainability 2022, 14(21), 14280; https://doi.org/10.3390/su142114280 - 1 Nov 2022

Cited by 8 | Viewed by 2309

Abstract

Artificial intelligence technology has enabled the manufacturing industry and actively guided its transformation and promotion for the past few decades. Injection molding technology is a crucial procedure in mechanical engineering and manufacturing due to its adaptability and dimensional stability. An essential step in the injection molding process is quality inspection and manual visual inspection is still used in conventional quality control, but this open-loop working method has issues with subjectivity and real-time monitoring capacity. This paper proposes an integrated “processing–matching–classification–diagnosis” concept based on machine vision and deep learning that allows for efficient and intelligent diagnosis of injection molding in complex scenarios. Based on eight categories of failure images of plastic components, this paper summarizes the theoretical method of processing fault categorization and identifies the various causes of defects from injection machines and molds. A template matching mechanism based on a new concept—arbitration function

J (ψ_{i j})

—provided in this paper, matches the edge features to achieve the initial classification of plastic components images. A conventional VGG16 network is innovatively upgraded in this work in order to further classify the unqualified plastic components. The classification accuracy of this improved VGG16 reaches 96.67%, which is better than the 53.33% of the traditional network. The accuracy, responsiveness, and resilience of the quality inspection are all improved in this paper. This work enhances production safety while promoting automation and intelligence of fault diagnosis in injection molding systems. Similar technical routes can be generalized to other industrial scenarios for quality inspection problems. Full article

(This article belongs to the Special Issue Sustainable Engineering: Intelligent Design and New Material Development for Automobiles)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Sustainable Engineering: Intelligent Design and New Material Development for Automobiles

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI