Bio-inspired Tech. on Demand
Bio-inspired design was introduced as an alternative method to encourage breakthrough innovations during design projects by stimulating analogical reasoning and thinking of designers. However, the method did not perform as well as researchers expected because most designers, who are novices in the fields of biology and ecology, cannot infer the proper analogue (i.e. biological system) from nature. To resolve this fundamental problem, a causal model based representation framework for ‘analogical reasoning’ – searching and selecting the biological systems to apply – have been developed. In addition, ontology based repository structures and retrieval systems have been proposed to support ‘analogical thinking’ of designers. Nevertheless, these systematic approaches still restrict the candidates and inevitably lose potential biological systems relevant to the design project, due to the ‘physical relation’ biased problem and the ambiguity of the indexing mechanism of both current representation frameworks and retrieval systems. For example, the causality based support system known as a robust representation framework for a single biological system, stores information of a biological system only by its internal ‘physical relations’ and retrieves biological systetabms only by the physical relevance. However, from the perspective of ecological thinking, the further relatedness of ‘physical, biological, and ecological relations’ composes the holistic concept used to identify an organism in the flow of evolution because the ‘biological and ecological relations’ are also involved in the traits that designers may be interested in. Therefore, the supplementary information for ‘biological and ecological relations’ must be added to index the biological and environmental interactions, and to use the connectivity among entire organisms in the retrieval process. In this research, a causality based holistic representation framework for biological systems and an ‘all-connected’ ontology based repository and retrieval system are developed as a knowledge-based recommendation system to support bio-inspired design. The knowledge-based system we developed allows engineering designers to search and select a particular biological system and extract design strategy without much biological knowledge. This effort provides more opportunities in a bio-inspired design process by adding potential biological systems that might previously not have been considered.