Date of Award

5-2013

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Mechanical Engineering

Advisor

Mocko, Gregory M

Committee Member

Fadel , Georges F

Committee Member

Ramasubramanian , Melur

Abstract

Biomimetic design offers an avenue for designers to expand the solution realm by offering surface dissimilar analogies. However, a significant challenge within biomimetic design has been offering a suitable method for discovery and retrieval of inspiring biological phenomena to aid in conceptual design. This research proposes an approach for classifying both biological systems and engineering products into the type of problem being addressed. This allows designers to search for inspirational phenomena based on the type of problem that they are trying to solve. Initially this classification is performed with product-phenomenon pairings that have already been attributed to biomimetic design from an online database of bio-inspired products. Three experiments are performed to develop and validate the set of classifications. These experiments tested designers' ability to classify biological phenomena, evaluated the classifications, and validated the correctness of the classification for each product-phenomenon pairing. The experiments resulted in a classification schema of six problem types: materials, machines, fluids and dynamics, heat transfer, mechanics of materials, and energy. The average Kappa is 0.73, which is significant agreement between raters. The product classification was performed by three separate raters and showed a high level of inter-rater agreement. Furthermore two raters classified the products using a primary and secondary classification schema. The primary and secondary classifications resulted in a Kappa value of 0.92. Future research work to complement the classification scheme is the identification of rules based from text mining of biologically inspired products. Specifically text mining approaches and Artificial Neural Networks (ANN), as well as a biological classification are integrated to discover relations.

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