Spiraling into Design Inspiration

Nature is a powerful resource for engineering designers. The natural world provides numerous cases for analogy and inspiration in engineering design. Transferring the valuable knowledge and inspiration gained from the biology domain to the engineering domain during concept generation is a somewhat disorganized process and relies heavily on the designers’ insight and background knowledge of many fields to make the necessary leaps between the domains. Furthermore, the novice designer approaching biology for inspiration tends to focus heavily on copying the visual attributes of a biological system to develop a solution that looks like the biological system rather than explore at deeper levels to uncover relationships that lead to the development of true analogies. There are now well-known methods for teaching bioinspired design in engineering and the majority of methods prescribe the use of analogies in order to facilitate knowledge transfer, however, guidance in analogy formulation to foster the creative leaps is missing or ill defined. Thus little is known about how students use biological systems for design inspiration. This paper proposes categories for analogical knowledge transfer in bio-inspired design to foster and characterize diverse analogical knowledge transfer. The proposed analogy categories are used to describe the behavior seen in an engineering class. Results indicate that (1) single biological system provides multiple analogies that result in different engineering inspiration for design; (2) biological information from multiple categories is transferred during concept generation; and (3) non-physical characteristics may inspire more sophisticated engineering inspiration than those based on physical characteristics alone. Overall, the analogy data classification has resulted in a better understanding of analogical knowledge transfer during bio-inspired design and leads to best practices for teaching bio-inspired design to engineering students.

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An East Central European folk aesthetic as a postmodern garment design inspiration

10.31274/rtd-20200622-69 ◽

2006 ◽

Author(s):

Jessica Ann Havlicek

Keyword(s):

East Central ◽

Central European ◽

Garment Design ◽

East Central European ◽

Design Inspiration

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Inspiring Engineering in the K12: Biomimicry As a Bridge Between Math and Biology

Volume 5: Engineering Education ◽

10.1115/imece2019-10248 ◽

2019 ◽

Author(s):

Fredrik Sanne ◽

Inge Risheim ◽

Thomas J. Impelluso

Keyword(s):

Research And Development ◽

Laboratory Experience ◽

The Third ◽

The World ◽

Quantitative Design ◽

Multi Phase ◽

Design Inspiration

Abstract The discipline of biomimicry encourages engineers to take design inspiration from the nearly four billion years of research and development since life first appeared on Earth — nature is the greatest engineering designer. Rather than leveraging biomimicry as a discipline unto itself (a worthy approach, regardless), this project explores biomimicry as a tool to inspire K12 students to appreciate math and engineering. We conducted this project in four lesson modules and one lab. In the first module, we presented various types of engineering. In the second, we introduced certain aspects of mathematics from a qualitative perspective. In the third, we discussed the fundamental mathematics that undergirds thermodynamics, although qualitatively and visually. In the fourth, we introduced the students to the world of biomimicry. Then we integrated the mathematics and biomimicry with a laboratory experience in quantitative design, borrowed from an NSF sponsored project. In summary, efforts in biomimicry reside at either the quantitative arena of multi-phase physics, or the qualitative arena of biological interpretations. However, we have used it as a bridge to science, math and engineering.

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Madhubani Painting Motifs: A Source of Design Inspiration for Traditional to Contemporary Designs

International Journal of Current Microbiology and Applied Sciences ◽

10.20546/ijcmas.2019.811.102 ◽

2019 ◽

Vol 8 (11) ◽

pp. 863-873

Author(s):

Pooja Rani ◽

Saroj Yadav ◽

Neelam M. Rose ◽

Vandana Bhandari

Keyword(s):

Design Inspiration

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Double attention recurrent convolution neural network for answer selection

Royal Society Open Science ◽

10.1098/rsos.191517 ◽

2020 ◽

Vol 7 (5) ◽

pp. 191517

Author(s):

Ganchao Bao ◽

Yuan Wei ◽

Xin Sun ◽

Hongli Zhang

Keyword(s):

Neural Network ◽

Question Answering ◽

Convolution Neural Network ◽

Deep Model ◽

Selection Tasks ◽

Matching Score ◽

Key Steps ◽

Chinese And English ◽

Design Inspiration ◽

Better Than

Answer selection is one of the key steps in many question answering (QA) applications. In this paper, a new deep model with two kinds of attention is proposed for answer selection: the double attention recurrent convolution neural network (DARCNN). Double attention means self-attention and cross-attention. The design inspiration of this model came from the transformer in the domain of machine translation. Self-attention can directly calculate dependencies between words regardless of the distance. However, self-attention ignores the distinction between its surrounding words and other words. Thus, we design a decay self-attention that prioritizes local words in a sentence. In addition, cross-attention is established to achieve interaction between question and candidate answer. With the outputs of self-attention and decay self-attention, we can get two kinds of interactive information via cross-attention. Finally, using the feature vectors of the question and answer, elementwise multiplication is used to combine with them and multilayer perceptron is used to predict the matching score. Experimental results on four QA datasets containing Chinese and English show that DARCNN performs better than other answer selection models, thereby demonstrating the effectiveness of self-attention, decay self-attention and cross-attention in answer selection tasks.

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