A knowledge graph-aided decision guidance method for product conceptual design

Abstract

In the fast-paced world of product design, businesses seek a competitive edge by swiftly addressing user requirements and developing precise solutions. Depending on diverse knowledge, conceptual design makes enterprises invest significantly in knowledge management. Following the philosophy of decision-based design, a knowledge graph-aided decision guidance method is proposed to streamline and enhance knowledge utilisation in product conceptual design. Firstly, the decision-making process in product conceptual design is modelled using the Concept-Decision-Knowledge (CDK) model, yielding the meta-model of CDK (mCDK). A data-augmented BERT-BiLSTM-CRF model is adopted to extract information from diverse data sources, forming a CDK knowledge graph (CDK-KG). A decision case for employing problem-solving knowledge configuration is generated to resolve specific design decision problems when new requirements arise. Validation of the method is demonstrated through a case study on the launch vehicle conceptual design of the first and second-stage separation system. The results indicate that the proposed method can automatically extract information from data resources to construct a knowledge graph. Furthermore, it can provide decision cases for design requirements through knowledge configuration, supporting decision-making.

KEYWORDS:

• Conceptual design
• design decision cognitive
• knowledge graph
• knowledge representation
• knowledge configuration

Acknowledgments

This paper is an outcome of the International Systems Realisation Partnership between the Institute for Industrial Engineering @ The Beijing Institute of Technology, China, The Systems Realisation Laboratory @ The University of Oklahoma, USA, the Design Engineering Laboratory @ Purdue, USA, and The Systems Realisation Laboratory @ The University of Liverpool, UK. Thanks for the support of the Key Laboratory of Industry Knowledge & Data Fusion Technology and Application, Ministry of Industry and Information Technology, Beijing Institute of Technology.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Replication of results

The codes to reproduce the results presented in this paper are available at the following link: https://github.com/BIT-SME-Sunyanshao/CDK_KG-Decision-Guidance.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number NSFC 52105241], Beijing Institute of Technology Research Fund Program for Young Scholars, and also financial support from the National Ministries Projects of China [grant number 50923010101, D020101].