A manufacturing-environmental model using Bayesian belief networks for assembly design decision support

Authors:
Wooi Ping Cheah;Kyoung-Yun Kim;Hyung-Jeong Yang;Sook-Young Choi;Hyung-Jae Lee
Affiliations:
Dept. of Computer Science, Chonnam National University, Gwangju, South Korea;Dept. of Indust. and Manuf. Eng., Wayne State University, Detroit, MI;Dept. of Computer Science, Chonnam National University, Gwangju, South Korea;Dept. of Computer Education, Woosuk University, Chonbuk, South Korea;Dept. of Computer Science, Chonnam National University, Gwangju, South Korea
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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Abstract

Assembly design decision making is to provide a solution of currently violating design by evaluating assembly design alternatives with the consideration of the assembly design decision (ADD) criteria and of the causal interactions with manufacturing-environmental factors. Even though existing assembly design support systems have a systematic mechanism for determining the decision-criterion weight, the system still has a limitation to capture the interactions between manufacturing-environmental factors and ADD criteria. Thus, we introduce in this paper, Bayesian belief networks (BBN) for the representation and reasoning of the manufacturing-environmental knowledge. BBN has a sound mathematical foundation and reasoning capability. It also has an efficient evidence propagation mechanism and a proven track record in industry-scale applications. However, it is less friendly and flexible, when used for knowledge acquisition. In this paper, we propose a methodology for the indirect knowledge acquisition, using fuzzy cognitive maps, and for the conversion of the representation into BBN.