Evaluating Quantitative Measures for Assessing Functional Similarity in Engineering Design

The development of example-based design support tools, such as those used for design-by-analogy, relies heavily on the computation of similarity between designs. Various vector- and graph-based similarity measures operationalize different principles to assess the similarity of designs. Despite the availability of various types of similarity measures and the widespread adoption of some, these measures have not been tested for cross-measure agreement, especially in a design context. In this paper, several vector- and graph-based similarity measures are tested across two datasets of functional models of products to explore the ways in which they find functionally similar designs. The results show that the network-based measures fundamentally operationalize functional similarity in a different way than vector-based measures. Based upon the findings, we recommend a graph-based similarity measure such as NetSimile in the early stages of design when divergence is desirable and a vector-based measure such as cosine similarity in a period of convergence, when the scope of the desired function implementation is clearer.

Eyes Toward Tomorrow Program Enhancing Collaboration, Connections, and Community Using Bioinspired Design

The goal of our Eyes Toward Tomorrow Program is to enrich the future workforce with STEM by providing students with an early, inspirational, interdisciplinary experience fostering inclusive excellence. We attempt to open the eyes of students who never realized how much their voice is urgently needed by providing an opportunity for involvement, imagination, invention, and innovation. Students see how what they are learning, designing, and building matters to their own life, community, and society. Our program embodies convergence by obliterating artificially created, disciplinary boundaries to go far beyond STEM or even STEAM by including artists, designers, social scientists, and entrepreneurs collaborating in diverse teams using scientific discoveries to create inventions that could shape our future. Our program connects two recent revolutions by amplifying Bioinspired Design with the Maker Movement and its democratizing effects empowering anyone to innovate and change the world. Our course is founded in original discovery. We explain the process of biological discovery and the importance of scaling, constraints, and complexity in selecting systems for bioinspired design. By spotlighting scientific writing and publishing, students become more science literate, learn how to decompose a biology research paper, extract the principles, and then propose a novel design by analogy. Using careful, early scaffolding of individual design efforts, students build the confidence to interact in teams. Team building exercises increase self-efficacy and reveal the advantages of a diverse set of minds. Final team video and poster project designs are presented in a public showcase. Our program forms a student-centered creative action community comprised of a large-scale course, student-led classes, and a student-created university organization. The program structure facilitates a community of learners that shifts the students' role from passive knowledge recipients to active co-constructors of knowledge being responsible for their own learning, discovery, and inventions. Students build their own shared database of discoveries, classes, organizations, research openings, internships, and public service options. Students find next step opportunities so they can see future careers. Description of our program here provides the necessary context for our future publications on assessment that examine 21st century skills, persistence in STEM, and creativity.

Aligning Human and Computational Evaluations of Functional Design Similarity

Function drives many early design considerations in product development. Therefore, finding functionally similar examples is important when searching for sources of inspiration or evaluating designs against existing technology. However, it is difficult to capture what people consider to be functionally similar and therefore, if measures that compare function directly from the products themselves are meaningful. In this work, we compare human evaluations of similarity to computationally determined values, shedding light on how quantitative measures align with human perceptions of functional similarity. Human perception of functional similarity is considered at two levels of abstraction: (1) the high-level purpose of a product, and (2) a detailed view of how the product works. Human evaluations of similarity are quantified by crowdsourcing 1360 triplet ratings at each functional abstraction, and then compared to similarity that is computed between functional models. We demonstrate how different levels of abstraction and the fuzzy line between what is considered “similar” and “similar enough” may impact how these similarity measures are utilized, finding that different measures better align with human evaluations along each dimension. The results inform how product similarity can be leveraged by designers. Therefore, applications lie in creativity support tools, such as those used for design-by-analogy, or future computational methods in design that incorporate product function in addition to form.

Data-Driven Design-by-Analogy: State of the Art

Design-by-Analogy (DbA) is a design methodology that draws inspiration from a source domain to a target domain to generate new solutions to problems or designs, which can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. Herein, we survey the prior data-driven DbA studies and categorize and analyze individual study according to the data, methods and applications in four categories including analogy encoding, retrieval, mapping, and evaluation. Based on such structured literature analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field.

DIFFERENCES BETWEEN BEGINNING AND ADVANCED STUDENTS USING SPECIFIC ANALOGICAL STIMULI DURING DESIGN-BY-ANALOGY

Studies reported the effects of different types and different levels of abstraction of analogical stimuli on designers. However, specific, single visual analogical stimuli on the effects of designers have not been reported. We define this type of stimuli as specific analogical stimuli. We used the extended linkography method to analyze the facilitating and limiting effects of specific analogical stimuli and free association analogical stimuli (nonspecific analogical stimuli) on the students' creativity at different design levels. The results showed that: (1) Advanced students focused on exploring the depth of the design problem while beginning students tended to explore the breadth of the design problem. (2) Nonspecific analogical stimuli enhanced the creativity of beginning students. However, its impact on advanced students is less pronounced. (3) The specific analogical stimuli attract the students into design fixation. Furthermore, it has a more pronounced effect on advanced students. These results illustrate the differences in the effects of specific analogy stimuli on the students at different design levels. It clarifies the use of analogical stimuli in design and the teaching of analogical design methods in design education.

Data-Driven Design-by-Analogy: State of the Art and Future Directions

Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.

Identifying Design Analogies thru Models of Function, Flow and Performance

Design by analogy demonstrates itself as a powerful, yet very cognitively challenging approach for innovative design. Highly effective computational tools with well-structured repositories of analogues are needed to support this cognitively difficult task and to further spur innovation building effectively on past solutions. The majority of tools have focused on linguistic computational tools. This chapter seeks to support the development of highly effective computational tools that employ a functional modeling basis.

Deriving design feature vectors for patent images using convolutional neural networks

The patent database is often used by designers to search for inspirational stimuli for innovative design opportunities because of the large size, extensive variety and the massive quantity of design information contained in patent documents. Growing work on design-by-analogy has adopted various vectorization approaches for associating design documents. However, they only focused on text analysis and ignored visual information. Research in engineering design and cognitive psychology has shown that visual stimuli may benefit design-by-analogy. In this study, we focus on visual design stimuli and automatically derive the vector space and the design feature vectors representing design images. The automatic vectorization approach uses a novel convolutional neural network architecture named Dual-VGG aiming to accomplish two tasks: visual material type prediction and international patent classification (IPC) section-label predictions. The derived feature vectors that embed both visual characteristics and technology-related knowledge can be potentially utilized to guide the retrieval and use of near-field and far-field design stimuli according to their vector distances. We report the accuracy of the training tasks and also use a case study to demonstrate the advantages of design image retrievals based on our model.