Automotive Lightweight Design Modeling And Intelligent Optimization Learn Key Technologies

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Gejing Xu, Wei Liang, Jiahong Cai, Jiahong Xiao, Xingyu Chen, Yinyan Gong
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引用次数: 0

Abstract

The automotive industry has always been seeking innovative solutions to improve car performance, safety, and cost savings. Lightweight design technology has become one of the solutions. This article summarizes the modeling and optimization methods of automotive lightweight design, as well as key technologies based on intelligent optimization learning. First, this article outlines the basic concepts of automotive lightweight design, as well as the needs and challenges of the industry for lightweight design. Then, the modeling methods of lightweight design are introduced in detail, including geometric modeling, topology optimization, structural optimization, and multidisciplinary optimization. At the same time, commonly used materials, manufacturing processes, and testing methods in lightweight design are introduced, as well as relevant design guidelines and standards. This article also introduces some algorithms and their applicable scenarios. Additionally, this article summarizes the application prospects and future development directions of key technologies for automotive lightweight design modeling and intelligent optimization learning. We emphasize the opportunities and challenges in this field and propose how to continue promoting the development of lightweight design technology and responding to increasingly complex market demands. This article provides a systematic review of key technologies for automotive lightweight design modeling and intelligent optimization learning, which helps researchers and practitioners to deepen their understanding of the technical development and application trends in this field.
汽车轻量化设计建模与智能优化学习关键技术
汽车行业一直在寻求创新的解决方案,以提高汽车的性能、安全性和成本节约。轻量化设计技术已成为解决方案之一。本文综述了汽车轻量化设计的建模与优化方法,以及基于智能优化学习的关键技术。首先,本文概述了汽车轻量化设计的基本概念,以及行业对轻量化设计的需求和挑战。然后,详细介绍了轻量化设计的建模方法,包括几何建模、拓扑优化、结构优化和多学科优化。同时介绍了轻量化设计中常用的材料、制造工艺和测试方法,以及相关的设计指南和标准。本文还介绍了一些算法及其应用场景。总结了汽车轻量化设计建模和智能优化学习关键技术的应用前景和未来发展方向。我们强调了这一领域的机遇和挑战,并提出了如何继续推动轻量化设计技术的发展,以应对日益复杂的市场需求。本文对汽车轻量化设计建模和智能优化学习的关键技术进行了系统综述,有助于研究人员和从业人员加深对该领域技术发展和应用趋势的理解。
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来源期刊
Journal of Cloud Computing-Advances Systems and Applications
Journal of Cloud Computing-Advances Systems and Applications Computer Science-Computer Networks and Communications
CiteScore
6.80
自引率
7.50%
发文量
76
审稿时长
75 days
期刊介绍: The Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA) will publish research articles on all aspects of Cloud Computing. Principally, articles will address topics that are core to Cloud Computing, focusing on the Cloud applications, the Cloud systems, and the advances that will lead to the Clouds of the future. Comprehensive review and survey articles that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant.
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