利用生成式人工智能生成受 TRIZ 启发的生态设计指南

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI:10.1016/j.aei.2024.102846

C.K.M. Lee , Jingying Liang , K.L. Yung , K.L. Keung

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引用次数: 0

摘要

在生态设计构思过程中，环境因素正在成为创新的刺激因素。将 TRIZ（Teoriya Resheniya Izobretatelskikh Zadatch──发明性问题解决理论）方法融入生态设计，为应对可持续性挑战提供了一种结构化的问题解决方法。然而，开发创新设计需要 TRIZ 概念方面的专业知识和资源，这使其成为一个耗时的过程，并可能限制其在生态设计创新中的快速应用。本研究利用大型语言模型（LLMs）的分析和生成能力来增强TRIZ方法，并使生态设计中的构思过程自动化。研究设计了一个智能工具 "生态创新助手"，通过设计草图为用户提供生态创新解决方案。通过比较研究对其有效性进行了验证和评估。研究结果表明了 LLM 在设计流程自动化方面的潜力，催化了人工智能驱动的创新和生态设计构思的变革。

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Generating TRIZ-inspired guidelines for eco-design using Generative Artificial Intelligence

Environmental considerations are emerging as stimuli for innovation during the eco-design ideation process. Integrating TRIZ (Teoriya Resheniya Izobretatelskikh Zadatch─Theory of Inventive Problem Solving) methodology into eco-design offers a structured problem-solving approach to address sustainability challenges. However, developing innovative designs requires expertise in TRIZ concepts and access to resources, which makes it a time-consuming process and can limit its application for eco-design innovation quickly. This study leverages the analytical and generative capabilities of large language models (LLMs) to enhance the TRIZ methodology and automate the ideation process in eco-design. An intelligent tool, “Eco-innovate Assistant,” is designed to provide users with eco-innovative solutions with design sketches. Its effectiveness is validated and evaluated through comparative studies. The findings demonstrate the potential of LLMs in automating design processes, catalyzing a transformation in AI-driven innovation and ideation in eco-design.

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来源期刊

Advanced Engineering Informatics 工程技术-工程：综合

CiteScore

12.40

自引率

18.20%

发文量

292

审稿时长

45 days

期刊介绍： Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.