Current supportive bio-inspired design methods focus on handcrafting the inspiration engineers use to speed up bio-inspired design. However promising, such methods are not scalable as the time investment is shifted to an up-front investment. Furthermore, most proposed methods require the engineer to adopt a new design process. The current study presents FISh, a scalable search method based on the standard engineering design process. By leveraging machine translation between a representative corpus of biological and engineering texts, the engineer can start the search using engineering terminology, which, behind the scenes, is automatically converted to a biological query. This conversion is done using language models trained on patents and biological publications for the engineering and biology domains. Both models are aligned using the most used English words. The biological query is used to retrieve biological documents that describe the most relevant functionality for the engineering query. The presented method allows searching for bio-inspiration using a free-text query. Furthermore, updating the underlying datasets, models and organism aspects is automated, allowing the system to stay up to date without requiring interactive effort. Finally, the search functionality is validated by comparing the search results for the functionality of existing bio-inspired designs with their inspiring organisms.

Design fixation refers to the designers’ inability to avoid becoming stuck with preexisting ideas in order to generate new ones. With the recent fast advancements and developments, XR has emerged as a powerful promising technology that can shed new light on this issue. Consequently, this paper aims at: (1) investigating the underlying mechanisms of design fixation as reported in literature; (2) exploring the state-of-art in the use of XR technology in design; and (3) identifying ways to mitigate design fixation by employing XR technology.

The biological domain has the potential to offer a rich source of analogies to solve engineering design problems. However, due to the complexity embedded in biological systems, adding to the lack of structured, detailed, and searchable knowledge bases, engineering designers find it hard to access the knowledge in the biological domain, which therefore poses challenges in understanding the biological concepts in order to apply these concepts to engineering design problems. In order to assist the engineering designers in problem-solving, we report, in this paper, a web-based tool called Idea-Inspire 4.0 that supports analogical design using two broad features. First, the tool provides access to a number of biological systems using a searchable knowledge base. Second, it explains each one of these biological systems using a multi-modal representation: that is, using function decomposition model, text, function model, image, video, and audio. In this paper, we report two experiments that test how well the multi-modal representation in Idea-Inspire 4.0 supports understanding and application of biological concepts in engineering design problems. In one experiment, we use Bloom's method to test “analysis” and “synthesis” levels of understanding of a biological system. In the next experiment, we provide an engineering design problem along with a biological-analogous system and examine the novelty and requirement-satisfaction (two major indicators of creativity) of resulting design solutions. In both the experiments, the biological system (analogue) was provided using Idea-Inspire 4.0 as well as using a conventional text-image representation so that the efficacy of Idea-Inspire 4.0 is tested using a benchmark.

This paper presents a computational design method to analyze and synthesize representation mechanisms in structural design. The role of shape grammar schemas in analyzing parametric and grammatical structural analysis is discussed, and a set of schema to generate novel structural topology is provided.

If a solvable problem is currently unsolved, then something important to a solution is most likely being overlooked. From this simple observation we derive the obscure features hypothesis: every innovative solution is built upon at least one commonly overlooked or new (i.e., obscure) feature of the problem. By using a new definition of a feature as an effect of an interaction, we are able to accomplish five things. First, we are able to determine where features come from and how to search for new ones. Second, we are able to construct mathematical arguments that the set of features of an object is not computably enumerable. Third, we are able to characterize innovative problem solving as looking for a series of interactions that produce the desired effects (i.e., the goal). Fourth, we are able to construct a precise problem-solving grammar that is both human and machine friendly. Fifth, we are able to devise a visual and verbal problem-solving representation that both humans and computers can contribute to as they help counteract each other's problem-solving weaknesses. We show how computers can counter some of the known cognitive obstacles to innovation that humans have. We also briefly discuss ways in which humans can return the favor. We conclude that a promising process for innovative problem solving is a human–computer collaboration in which each partner assists the other in unearthing the obscure features of a problem.

As more and more people are increasingly turning to nature for design inspiration, tools and methodologies are developed to support the systematic bioideation process. State-of-the-art approaches struggle with expanding their knowledge bases because of interactive work required by humans per biological strategy. As an answer to this persistent challenge, a scalable search for systematic biologically inspired design (SEABIRD) system is proposed. This system leverages experience from the product aspects in design by analogy tool that identifies candidate products for between-domain design by analogy. SEABIRD is based on two conceptual representations, product and organism aspects, extracted from, respectively, a patent and a biological database, that enable leveraging the ever growing body of natural-language biological texts in the systematic bioinspired design process by eliminating interactive work by humans during corpus expansion. SEABIRD's search is illustrated and validated with three well-known biologically inspired design cases.

The term design fixation is often used interchangeably to refer to situations where designers limit their creative output because of an overreliance on features of preexisting designs, or more generally, an overreliance on a specific body of knowledge directly associated with a problem. In this paper, we argue that interdisciplinary interest in design fixation has led to increasingly broad definitions of the phenomenon that may be undermining empirical research efforts, educational efforts to minimize fixation, and the acquisition and dissemination of transdisciplinary knowledge about fixation effects. To address these issues, we recommend that researchers consider categorizing fixation phenomena into one of three classifications: unconscious adherence to the influence of prior designs, conscious blocks to change, and intentional resistance to new ideas. Next, we distinguish between concept-based design fixation, fixation to a specific class of known design concepts, and knowledge-based design fixation, fixation to a problem-specific knowledge base. With these distinctions in place, we propose a system of orders of design fixation, recommend methods for reducing fixation in inventive design, and recommend areas that are in need of further research within the field of design science.

This paper presents a bioinspiration approach that is able to scalably leverage the ever-growing body of biological information in natural-language format. The ideation tool AskNature, developed by the Biomimicry 3.8 Institute, is expanded with an algorithm for automated classification of biological strategies into the Biomimicry Taxonomy, a three-level, hierarchical information structure that organizes AskNature's database. In this way, the manual work entailed by the classification of biological strategies can be alleviated. Thus, the bottleneck is removed that currently prevents the integration of large numbers of biological strategies. To demonstrate the feasibility of building a scalable bioideation system, this paper presents tests that classify biological strategies from AskNature's reference database for those Biomimicry Taxonomy classes that currently hold sufficient reference documents.