A smart knowledge deployment method for the conceptual design of low-carbon products

Guo, Xin, Zhao, Wu, Hu, Huicong, Li, Li, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Wang, Jie and Zhang, Kai 2021. A smart knowledge deployment method for the conceptual design of low-carbon products. Journal of Cleaner Production 321 , 128994. 10.1016/j.jclepro.2021.128994

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Abstract

As the consciousness of the global environment and sustainability has increased, low-carbon products have played a vital role in the transformation to a circular economy. Advanced smart design technology has enabled product designers to fulfill customer requirements by offering tailor-made functions and low-carbon solutions. However, although the existing approaches used in the conceptual design process can help in functional reasoning, knowledge modelling, and scheme evaluation, the smart reuse of knowledge, such as in design model improvement and concept scheme iteration for lower carbon emission, the corresponding process evaluation concerning carbon footprint has not been given sufficient attention. To resolve this, in this work, a smart knowledge deployment method is proposed for reasoning, configuring, and optimizing the conceptual scheme (CS) based on carbon emission evaluation and interaction. First, to match discretized knowledge, sub-function requirements after function decomposition are mapped with granular clustered knowledge into a matrix based on a requirement function knowledge deployment (RFKD) model. Second, the derived candidate concept schemes (CCSs) are selected in three steps: conflict-based primaries, configuration, and carbon footprint ranking. Finally, the initial conceptual scheme (ICS) with the lowest carbon emission is used as input for the interactive genetic algorithm (IGA) to better capture a comprehensive set of user feedback on potential candidate schemes through interactions. Accordingly, improvements are completed as intended. The prototype design and an experimental study of a brand-new friction-wear testing machine are conducted. The results suggest that the proposed approach could effectively reduce the carbon emissions of products obtained through CS and improve the convergence of the schemes produced via genetic operation.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0959-6526
Date of First Compliant Deposit:	13 September 2021
Date of Acceptance:	9 September 2021
Last Modified:	07 Nov 2023 05:55
URI:	https://orca.cardiff.ac.uk/id/eprint/144058

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