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Intelligent product-gene acquisition method based on K-means clustering and mutual information-based feature selection algorithm

Published online by Cambridge University Press:  08 November 2019

Pan Li Open the ORCID record for Pan Li [Opens in a new window] ,
Yanzhao Ren ,
Yan Yan  and
Guoxin Wang
Pan Li
Affiliation:
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
Yanzhao Ren
Affiliation:
College of Information and Electrical Engineering, China Agricultural University, Beijing, China
Yan Yan
Affiliation:
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
Guoxin Wang*
Affiliation:
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
*
Author for correspondence: Guoxin Wang, E-mail: wangguoxin@bit.edu.cn
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Abstract

Conceptual design is a key stage of product design and has received increasing attention in recent years. However, this stage is characterized by limited information, large uncertainty, and multidisciplinary aspects. Thus, increased workload and time cost are associated with conceptual design information acquisition; sometimes, it is difficult to develop novel solutions and the feasibility of the solutions obtained according to these limited and uncertain information is difficult to guarantee. Genetics-based design (GBD) is an effective approach to develop novel solutions and improve the reuse of knowledge, which is consistent with the goal of the conceptual design process. Product-gene acquisition is the premise and basis of GBD. At present, there are few reported studies in this area; most of the existing works are constrained by the structural aspects of the acquisition process, and there are limited studies on specific implementation techniques. To explore the specific implementation technologies of product-gene acquisition, an intelligent acquisition method based on K-means clustering and mutual information-based feature selection algorithm is proposed in this paper. The product genes defined in this paper are key product information that determines the nature of the product and influences the conceptual design process. Thus, solutions obtained according to them are more feasible than that based on limited and uncertain information. An illustrative example is presented. The results show that the proposed method can achieve intelligent acquisition of product genes to a certain extent. Further, the proposed method will allow designers to quickly search for the corresponding product genes when performing similar functional design tasks.

Keywords

Conceptual designfeature selectionK-meansmutual informationproduct genes
Type
Research Article
Information
AI EDAM , Volume 33 , Special Issue 4: Intelligent Interaction Design , November 2019 , pp. 469 - 483
Copyright
Copyright © Cambridge University Press 2019

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