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.
This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion Fund) (KRF-2006-003-D00511).
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Cheah, W.P., Kim, KY., Yang, HJ., Choi, SY., Lee, HJ. (2007). A Manufacturing-Environmental Model Using Bayesian Belief Networks for Assembly Design Decision Support. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_37
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DOI: https://doi.org/10.1007/978-3-540-73325-6_37
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