Review of empowering computer-aided engineering with artificial intelligence

IF 3.9 2区工程技术 Q2 ENGINEERING, MANUFACTURING

Advances in Manufacturing Pub Date : 2025-03-14 DOI:10.1007/s40436-025-00545-0

Xu-Wen Zhao, Xiao-Meng Tong, Fang-Wei Ning, Mao-Lin Cai, Fei Han, Hong-Guang Li

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引用次数: 0

Abstract

Computer-aided engineering (CAE) is widely used in the industry as an approximate numerical analysis method for solving complex engineering and product structural mechanical performance problems. However, with the increasing complexity of structural and performance requirements, the traditional research paradigm based on experimental observations, theoretical modeling, and numerical simulations faces new scientific problems and technical challenges in analysis, design, and manufacturing. Notably, the development of CAE applications in future engineering is constrained to some extent by insufficient experimental observations, lack of theoretical modeling, limited numerical analysis, and difficulties in result validation. By replacing traditional mathematical mechanics models with data-driven models, artificial intelligence (AI) methods directly use high-dimensional, high-throughput data to establish complex relationships between variables and capture laws that are difficult to discover using traditional mechanics research methods, offering significant advantages in the analysis, prediction, and optimization of complex systems. Empowering CAE with AI to find new solutions to the difficulties encountered by traditional research methods has become a developing trend in numerical simulation research. This study reviews the methods and applications of combining AI with CAE and discusses current research deficiencies as well as future research trends.

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用人工智能赋能计算机辅助工程综述

计算机辅助工程（CAE）作为解决复杂工程和产品结构力学性能问题的近似数值分析方法，在工业上得到了广泛的应用。然而，随着结构和性能要求的日益复杂，传统的基于实验观察、理论建模和数值模拟的研究范式在分析、设计和制造方面面临着新的科学问题和技术挑战。值得注意的是，CAE在未来工程中应用的发展在一定程度上受到实验观测不足、理论建模缺乏、数值分析有限以及结果验证困难的制约。人工智能（AI）方法用数据驱动模型取代传统的数学力学模型，直接利用高维、高通量的数据建立传统力学研究方法难以发现的变量之间的复杂关系和捕获规律，在复杂系统的分析、预测和优化方面具有显著优势。利用人工智能赋予CAE以解决传统研究方法遇到的困难已成为数值模拟研究的发展趋势。本文综述了人工智能与CAE相结合的方法和应用，讨论了目前研究的不足和未来的研究趋势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advances in Manufacturing Materials Science-Polymers and Plastics

CiteScore

9.10

自引率

3.80%

发文量

274

期刊介绍： As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.