工程设计中的图形转换:近十年的概述

IF 1.7 3区 工程技术 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing Pub Date : 2023-02-02 DOI:10.1017/S089006042200018X
Christopher Voss, F. Petzold, S. Rudolph
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引用次数: 6

摘要

摘要在工程和体系结构中,已经开发了不同的方法,它们共享使用图转换来自动化设计过程或通过计算设计综合来搜索设计解决方案。为了概述这些方法,我们对过去十年中发表的文章进行了回顾。对四十八篇文章进行了审查,以确定这些方法的异同。确定了设计问题表示方法开发和图转换处理的研究领域,以及图转换在工程、建筑和形状语法中的应用。研究了记录词汇和规则的不同方法。最后,分析了不同的规则应用方法。基于发现的局限性,提出了未来的研究方向。
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Graph transformation in engineering design: an overview of the last decade
Abstract In engineering and architecture, different approaches have been developed that share the use of graph transformation to automate design processes or to search for design solutions by means of computational design synthesis. In order to give an overview of these approaches, we provide a review of articles published in the last decade. Forty-eight articles were reviewed to determine similarities and differences of these approaches. Research fields in method development for the representation of design problems and the processing of graph transformations, as well as the application of graph transformations in engineering, architecture, and shape grammars were identified. Different approaches for the documentation of the vocabulary and the rules were examined. Finally, different approaches for rule applications were analyzed. Based on found limitations, future research directions are suggested.
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来源期刊
CiteScore
4.40
自引率
14.30%
发文量
27
审稿时长
>12 weeks
期刊介绍: The journal publishes original articles about significant AI theory and applications based on the most up-to-date research in all branches and phases of engineering. Suitable topics include: analysis and evaluation; selection; configuration and design; manufacturing and assembly; and concurrent engineering. Specifically, the journal is interested in the use of AI in planning, design, analysis, simulation, qualitative reasoning, spatial reasoning and graphics, manufacturing, assembly, process planning, scheduling, numerical analysis, optimization, distributed systems, multi-agent applications, cooperation, cognitive modeling, learning and creativity. AI EDAM is also interested in original, major applications of state-of-the-art knowledge-based techniques to important engineering problems.
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