Bio-inspired generative design for engineering products: A case study for flapping wing shape exploration.

Nature has undergone millions of years of evolution, enabling organisms to adapt and survive in their environment. The remarkable features developed by these organisms serve as a rich source of inspiration for designers involved in engineering design. However, the effective application of bio-inspired design faces challenges due to the gap between biology and engineering, as well as the limited level of design automation. This paper proposes a bio-inspired generative design framework (BIGD), containing three main steps of dataset building, generator modeling, and design evaluation, which aims to automatically produce innovative designs by synthesizing a diverse range of natural designs using deep generative models. Specifically, a computational workflow is established for automated bio-inspired wing shape synthesis. The process of constructing a dataset typically involves web crawling data from various sources, followed by data preprocessing to ensure clarity. The data is then structured with prior knowledge from the biological domain and outliers are excluded. Finally, design knowledge is extracted through data mining and knowledge extraction techniques. In the case of a flapping wing shape design, the deep generative model is constructed using a bird wing dataset created with a strong foundation in biological domain knowledge. The wings generated by BIGD exhibit superior lift performance across a range of working conditions, showcasing the advantages of using BIGD to directionally guide the evolution of generative models based on biological knowledge. This research highlights the potential of using computational methods in bio-inspired design to rapidly generate innovative and high-performance designs. [ABSTRACT FROM AUTHOR]