生成设计
生成语法
变压器
计算机科学
人工智能
类比
机器学习
工程类
运营管理
语言学
电气工程
哲学
电压
公制(单位)
作者
Qihao Zhu,Xinyu Zhang,Jianxi Luo
出处
期刊:Journal of Mechanical Design
日期:2023-01-17
卷期号:145 (4)
被引量:8
摘要
Abstract Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a specific form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a data-driven approach to bridge the gap. This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely generative pre-trained transformer 3 (GPT-3), is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the mapping relevancy between the domains within the generated BID concepts. The approach is evaluated and then employed in a real-world project of designing light-weighted flying cars during its conceptual design phase The results show our approach can generate BID concepts with good performance.
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