Semantic Reasoning of Product Biologically Inspired Design Based on BERT

Bionic reasoning is a significant process in product biologically inspired design (BID), in which designers search for creatures and products that are matched for design. Several studies have tried to assist designers in bionic reasoning, but there are still limits. Designers’ bionic reasoning thinking in product BID is vague, and there is a lack of fuzzy semantic search methods at the sentence level. This study tries to assist designers’ bionic semantic reasoning in product BID. First, experiments were conducted to determine the designer’s bionic reasoning thinking in top-down and bottom-up processes. Bionic mapping relationships, including affective perception, form, function, material, and environment, were obtained. Second, the bidirectional encoder representations from transformers (BERT) pretraining model was used to calculate the semantic similarity of product description sentences and biological sentences so that designers could choose the high-ranked results to finish bionic reasoning. Finally, we used a product BID example to show the bionic semantic reasoning process and verify the feasibility of the method.

Evaluating Search Methods for Identifying Candidates for Novel Biologically Inspired Sunscreens

As the Earth's ozone layer, which blocks harmful UV radiation from reaching Earth's surface, becomes thinner, sunscreens have become one the most used and most important cosmetic products in recent decades. However, UV filters (both organic and inorganic) in the current synthetic sunscreen are found to affect the marine ecosystem negatively. They pollute the marine environment and cause damage to organisms’ reproductive and digestive systems (from microorganisms to mammals). Here, we use biologically inspired design with a problem and sustainability-driven approach to identify a possible plant or animal-inspired ingredients that could be extracted and used to obtain high protection from the sun while minimizing toxicity to human skin and pollution of the marine environment. We used Google, Google Scholar, and AskNature to identify solutions and evaluate their effectiveness. While the three search methods did not differ statistically in the number of initial solutions found, Google was perhaps surprisingly the one that generated the most hits. However, in terms of the most promising solutions, AskNature generated the most. We identified nine solutions with a particularly high potential based on their UV absorption and potential for mass production for further development: raspberry seed oil, nanoparticles of ivy rootlets, cuticular wax of dwarf mountain pine, lichen extract, MAAs from cyanobacteria, MAAs from dinoflagellates, red sweat of hippopotamus, mucus from mushroom coral, and gadusol compound from marine organisms of which we found that nanoparticles of ivy rootlets, lichen extracts, and gadusol to be especially promising.

Getting Beyond the Hairy House: Using Structure-Function-Mechanism to Advance Biologically Inspired Design Pedagogy

In this case study we report on the use of a Next Generation Science Standards (NGSS)-aligned form of Structure-Behavior-Function, called Structure-Function-Mechanism (SFM), to teach four high school engineering teachers an approach for Biologically Inspired Design (BID). Functional theories of design describe a natural way in which designers solve design problems. They provide support for case-based and analogical-based reasoning systems and have been used successfully to teach BID to undergraduate students. We found that teachers instructed on BID practice and pedagogy using our modified theory were able to grasp the structural concepts and looked for clear markers separating mechanism (behavior) and function. Because of the systems-of-systems nature of most biological entities, these boundaries were often subjective, presenting unique challenge to teachers. As high school engineering teachers look for methods to enhance their pedagogy and to understand multidisciplinary content, these findings will inform future curriculum development and professional learning approaches for engineering education.

Learning From Insects to Increase Multi-Agent System Resilience: Functional Decomposition and Transfer to Support Biologically Inspired Design

Resilience is an emergent property of complex systems that describes the ability to detect, respond, and recover from adversity. Much of the modern world consists of multiple, interacting, and independent agents (i.e. Multi-Agent Systems). However, the process of improving Multi-Agent System resilience is not well understood. We seek to address this gap by applying Biologically Inspired Design to increase complex system resilience. Eusocial insect colonies are an ideal case study for system resilience. Although individual insects have low computing power, the colonies collectively perform complex tasks and demonstrate resilience. Therefore, analyzing key elements of eusocial insect colonies may offer insight on how to increase Multi-Agent System resilience. Before the strategies used in eusocial insects can be adapted for Multi-Agent Systems, however, the existing research must be identified and transferred from the biological sciences to the engineering field. These transfers often hinder or limit biologically inspired design. This paper translates the biological investigation of individual insects and colony network behavior into strategies that can be tested to increase Multi-Agent System resilience. These strategies are formulated to be applied to Agent-Based Modeling. Agent-Based Modeling has been applied to many Multi-Agent Systems including epidemiology, traffic management, and marketing. This provides a key step in the design-by-analogy process: Identifying and decoding analogies from their original context. The design principles proposed in this work provide a foundation for future testing and eventual implementation into Multi-Agent Systems.

THE RELATIONSHIP BETWEEN FUNCTIONS AND OUTCOMES OF BIOLOGICALLY-INSPIRED DESIGN

Research in biologically-inspired design (BID) practice often focus on team composition or ideation based on an already discovered fascinating biological solution principle. However, how are the outcome of the early design phases affecting BID projects' quality?In this study, historical data from 91 reports from student teams documenting their BID efforts from a 3-week course constitute the data source. Thus, the relationship between design problem types, function types, functions descriptions and BID projects' quality is addressed.The study show that especially design problem types and function descriptions affect the BID projects' quality. For instance, BID projects dealing with open-ended problems yield better results than redesign problems with existing solutions operating in a very domain-limited solution space. Next, BID projects obtain the best results when using functions as drivers for analogy searching rather than properties. Finally, BID projects with certain function types seem to have more complicated conceptualization phases.