Knowledge-based engineering approach for defining robotic manufacturing system architectures - Université de technologie de Compiègne Access content directly
Journal Articles International Journal of Production Research Year : 2022

Knowledge-based engineering approach for defining robotic manufacturing system architectures

Abstract

Robotic manufacturing systems have proven to be an effective solution for modern manufacturing enterprises to deal with increasing in customer demands and market competition. However, these systems may be unable to completely satisfy user requirements because of the difference between user and design perspectives. Thus, designing robotic manufacturing systems requires iterative processes that significantly increase development costs and lead time. A user-customised design approach is needed that enables users to customise robotic manufacturing systems as well as alleviate the burden on designers of eliciting user requirements. However, most users may not be able to customise their systems because of a lack of engineering knowledge. The authors propose a knowledge-based engineering approach to aid users in customising the architectures of robotic manufacturing systems. Two models — an ontological knowledge model and a multi-attribute decision-making model — are defined and integrated in the proposed KBE architecture definition method. A rule-based reasoning process is proposed in the ontological knowledge model based on explicit semantic descriptions of users’ unstructured or semi-structured requirements and the components of robotic manufacturing systems, which infers the possible architecture of the required system. The MADM model is adopted to evaluate the architecture alternatives to determine the optimal solution.
No file

Dates and versions

hal-03847286 , version 1 (10-11-2022)

Identifiers

Cite

Chen Zheng, Yushu An, Zhanxi Wang, Xiansheng Qin, Benoît Eynard, et al.. Knowledge-based engineering approach for defining robotic manufacturing system architectures. International Journal of Production Research, 2022, pp.1-19. ⟨10.1080/00207543.2022.2037025⟩. ⟨hal-03847286⟩
34 View
0 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More