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 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.Comment: Accepted by J. Mech. Des. arXiv admin note: substantial text overlap
with arXiv:2204.0971