Title: Research on the intelligent design of the multidimensional form of a product under a brand cobweb constraint

Authors: Wenjin Yang; Jianning Su; Shutao Zhang; Kai Qiu; Pengfei Su; Xinxin Zhang

Addresses: School of Mechanical and Electrical Engineering, Lanzhou University of Technology, 730050, China ' School of Design Art, Lanzhou University of Technology, 730050, China ' School of Design Art, Lanzhou University of Technology, 730050, China ' School of Mechanical and Electrical Engineering, Lanzhou University of Technology, 730050, China ' School of Design Art, Lanzhou University of Technology, 730050, China ' School of Art, Design and Media, East China University of Science and Technology, 200237, China

Abstract: Based on the cobweb theory and bee evolutionary genetic algorithm (BEGA), a multidimensional style intelligent design method is proposed to inherit the brand characteristics and obtain a multidimensional product style during the design process. Cobweb theory is used to characterise the multidimensional development of product genealogy, analyse the product morphological evolution mechanism of past generations, and distinguish morphological genes. The efficient inheritance of the BEGA guarantees the selection, crossover and mutation of genetic features. Based on the cobweb model of a certain brand of tractors, different morphological gene types were distinguished. With a human-computer evaluation, a constraint model that reflects the brand culture and consumer personality requirements was constructed. The BEGA was used to establish a multidimensional style intelligent design method for a certain brand. A tractor is used as an example to explain the process, and a set of experiments are used to verify the feasibility and effectiveness of the method.

Keywords: product form; intelligent design; brand cobweb; bee evolutionary genetic algorithm; BEGA; multidimensional style; tractors.

DOI: 10.1504/IJART.2020.112648

International Journal of Arts and Technology, 2020 Vol.12 No.4, pp.352 - 374

Received: 17 Dec 2019
Accepted: 03 Sep 2020
Published online: 25 Jan 2021 *

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