Web-based design review of fuel pumps using fuzzy set theory

Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road, Hong Kong NA, People's Republic of China
Engineering Applications of Artificial Intelligence (Impact Factor: 2.21). 12/2002; 15(6):529-539. DOI: 10.1016/S0952-1976(03)00010-1


While product design and development has received significant attention over the past years, the activity of Design Review (DR) has not been dealt with at the equal level, despite its paramount importance. This can be easily observed from the scarce literature and ad hoc approach in industrial practices. Few Product Data Management (PDM) systems provide facilities for design review due to the lack of rigorous DR methodologies. This paper proposes a systematic and rigorous methodology for more efficient and effective DR by applying and extending the Suh’s well-known Theory of Axiomatic Design (TAD) into a Systematic Theory of Axiomatic design Review (STAR). The resulting STAR framework is well-structured and generic enough not only to act as the basis for developing a web-based design review portal in the extended enterprise, but also to accommodate specific design review techniques such as FuzzySTAR (Fuzzy Set Theory Approach to design Review) used for reviewing the fuel pump design case study.

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    • "The AD method provides a powerful design tool that can be easily understood and used by designers. AD has been successfully applied to various application areas, such as manufacturing system design [24] [25], process and product design [26] [27] [28], quality function deployment [29] [30], supply chain management [31], decision making [32] [33] [34] [35], etc. "
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    ABSTRACT: Quality function deployment (QFD) is a useful analyzing tool in product design and development. To solve the uncertainty or imprecision in QFD, numerous researchers have applied the fuzzy set theory to QFD and developed various fuzzy QFD models. Three issues are investigated by examining their models. First, the extant studies focused on identifying important engineering characteristics and seldom explored the subsequent prototype product selection issue. Secondly, the previous studies usually use fuzzy number algebraic operations to calculate the fuzzy sets in QFD. This approach may cause a great deviation in the result from the correct value. Thirdly, few studies have paid attention to the competitive analysis in QFD. However, it can provide product developers with a large amount of valuable information. Aimed at these three issues, this study integrates fuzzy QFD and the prototype product selection model to develop a product design and selection (PDS) approach. In fuzzy QFD, the α-cut operation is adopted to calculate the fuzzy set of each component. Competitive analysis and the correlations among engineering characteristics are also considered. In prototype product selection, engineering characteristics and the factors involved in product development are considered. A fuzzy multi-criteria decision making (MCDM) approach is proposed to select the best prototype product. A case study is given to illustrate the research steps for the proposed PDS method. The proposed method provides product developers with more useful information and precise analysis results. Thus, the PDS method can serve as a helpful decision-aid tool in product design.
    Applied Mathematical Modelling 01/2011; 35(1-35):482-496. DOI:10.1016/j.apm.2010.07.014 · 2.25 Impact Factor
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    • "In recent years, the AD methodology has applied over different cases under fuzzy environment which is socalled as FAD. Although the fundamental concept of t FAD theory is relatively new, the application trends over various themes have continued such as design review (Huang & Jiang, 2002), manufacturing system evaluation (Kulak & Kahraman, 2005a), equipment selection (Kulak, Durmusog ˘lu, & Kahraman, 2005), assessment of transportation companies (Kulak & Kahraman, 2005b), development of fuzzy decision tree (Liu & Pedrycz, 2007), and docking performance of shipyards (Celik, Kahraman, Cebi, & Er, in press). The invaluable outcomes of the relevant papers indicate that the fuzzy extensions over the AD principles have contributions especially in decision and management related themes. "
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    ABSTRACT: The model selection paradigm is one of the focused themes within decision science. This paper addresses the consistent solution of model selection issue on the basis of the fuzzy axiomatic design (FAD) methodology. Moreover, the developed FAD–based model selection interface (FAD–MSI) is performed over the critical ship management processes in order to assign suitable (multiple criteria decision-making) MCDM techniques even if commonly utilized hybrid approaches. The outcomes of this study encourage the maritime practitioners for the further researches towards analytical modelling of ship management processes.
    Expert Systems with Applications 04/2009; 36(3-36):6477-6484. DOI:10.1016/j.eswa.2008.07.038 · 2.24 Impact Factor
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    ABSTRACT: Product development involves multiple phases. Design review (DR) is an essential activity formally conducted to ensure a smooth transition from one phase to another. Such a formal DR is usually a multicriteria decision problem, involving multiple disciplines. This paper proposes a systematic framework for DR using fuzzy set theory. This fuzzy approach to DR is considered particularly relevant for several reasons. First, information available at early design phases is often incomplete and imprecise. Second, the relationships between the product design parameters and the review criteria cannot usually be exactly expressed by mathematical functions due to the enormous complexity. Third, DR is frequently carried out using subjective expert judgments with some degree of uncertainty. The DR is defined as the reverse mapping between the design parameter domain and design requirement (review criterion) domain, as compared with Suh's theory of axiomatic design. Fuzzy sets are extensively introduced in the definitions of the domains and the mapping process to deal with imprecision, uncertainty, and incompleteness. A simple case study is used to demonstrate the resulting fuzzy set theory of axiomatic DR.
    Artificial intelligence for engineering design analysis and manufacturing 09/2002; 16(4):291-302. DOI:10.1017/S0890060402164031 · 0.60 Impact Factor
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