Application of fuzzy set theory and back-propagation neural networks in progressive die design

Lin, Zone‐Ching; Chang, Hong‐Chan

doi:10.1016/0278-6125(96)84552-3

Cited by 23 publications

(6 citation statements)

References 9 publications

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“…The position and balance point of the fuzzy number is also inappropriate (see the example in Section 5.1). Despite these methodological problems, the FWA has been applied by several researches (Lin and Chang 1996, Zhou 1998, Kao and Liu 1999.…”

Section: Fuzzy Weighted Average (Fwa)mentioning

confidence: 99%

Application of new fuzzy-weighted average (NFWA) method to engineering design evaluation

Vanegas¹,

Labib²

2001

International Journal of Production Research

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Engineering design evaluation is characterized by imprecise (vague) importance and satisfaction levels of criteria, which are better treated as fuzzy variables rather than as subjective crisp variables. Nevertheless, operations on fuzzy numbers tend to increase unnecessarily the imprecision when they are combined for some applications, particularly, when a fuzzy-weighted average (FWA) is calculated. This paper proposes a novel method of operating on fuzzy numbers to obtain a fuzzyweighted average of desirability levels during design evaluation. The method produces overall desirability levels less imprecise and more realistic than those of the conventional FWA. Furthermore, the balance points of these fuzzy numbers seem to be more credible than those of the FWA. An example is presented, which demonstrates the advantages of the method developed. IntroductionDesign is a main concern in modern manufacturing companies. Enterprises need to invest in designing because they have to develop new products to remain competitive in the market of today and operate at a pro® t. However, not only by carrying out design will manufacturing companies be pro® table and competitive, but also they need to improve the eOE ectiveness of the design activity. For many years, manufacturing companies tended to concentrate on improving manufacturing processes (Birmingham et al. 1997), but now they realise the potential of improvements and cost reduction during the design process. A study conducted by Lotter (1986) indicates that as much as 75% of the costs of a product is de® ned during the design stage. Therefore, it is here, in the execution and management of the design process, that signi® cant, potential reductions of cost can be made.To exploit all the potential of the design activity, companies have to understand well how to carry out this process; however, what they face is a very complex and not well understood design. Time to introduce a new product or artefact into the market and the design and manufacturin g costs have to be maintain at a minimum; also, the product has to have the right quality in order that the company can have success. Then, designers have to explore divergently such that several design solutions are considered to select the best design, that is, the design that satis® es the need with the lower costs and time to market, and the best quality.

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Section: Fuzzy Weighted Average (Fwa)mentioning

confidence: 99%

Application of new fuzzy-weighted average (NFWA) method to engineering design evaluation

Vanegas¹,

Labib²

2001

International Journal of Production Research

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“…The FWA method has been seen as an aggregated function for handling MCDM group decision problems based on fuzzy sets theory, and it has been successfully applied in many fields such as engineering, Tee and Bowman [28], Wang and Chang [31], Lin and Chang [21], Zhou [33], Vanegas and Labib [29], Chen et al [7], Wang et al [32], Vanicek et al [30], Zou and Xiao [35], Hermans et al [15], Liu et al [24], Ma et al [26], Zhu et al [34] and management science, e.g., Hon et al [16], Kao and Liu [17], Avineri et al [1], Lin et al [20], Liu [25], Ghazinoory et al [10] and Noor-E-Alam [27].…”

Section: Introductionmentioning

confidence: 99%

An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making

Lin

Hung

2011

Knowledge-Based Systems

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“…For engineering design, Lin and Chang [29] have used the FWA for progressive Manuscript [35] and Vanegas and Labib [34] applied the FWAs to evaluate material selection. Zhou [38] applied the FWA in quality function deployment (QFD).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Discrete Algorithms for Fuzzy Weighted Average

Chang

Hung

Lin

et al. 2006

IEEE Trans. Fuzzy Syst.

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Fuzzy weighted average (FWA), which can be applied to various fields such as engineering design, decision analysis, etc., and as function of fuzzy numbers, is suitable for the problem of multiple occurrences of fuzzy parameters. Additional fuzziness may be introduced in the -cut arithmetic. This paper reviews and compares discrete algorithms for the FWAs in both theoretical comparison and numerical comparisons, as opposed to the linear programming algorithms that may be efficient but require the help of linear programming software. An alternative efficient algorithm is also proposed. The algorithm introduces an all-candidate (criteria ratings) weights-replaced benchmark adjusting procedure other than a binary (dichotomy) search in the existing methods. The theoretical worst-case comparison shows that the algorithms of Guu and Guh et al. and our alternative algorithm require the same order of calculation complexity ( ), where is the number of FWA terms. Yet, the Guh et al. algorithm always requires 2 ( 1) calculations to be performed. The algorithm of Guu requires the elemental comparison complexity ( ), and is superior to the other algorithms, in the worst case. On the other hand, our alternative algorithm and Lee and Park algorithm have the same comparison complexity ( log ). Yet, our alternative algorithm requires ( ) calculation complexity which is better than ( log ) of the Lee and Park algorithm. The numerical experiments show that the algorithm of Guu and our alternative algorithm generally perform and converge faster by requiring fewer calculations than that of Lee and Park's algorithm. The alternative algorithm requires a slightly smaller number of calculations than that of the Guu algorithm, due to the use of the convergent benchmark adjustment procedure rather than a somewhat fixed binary search. Conversely, in the number of element comparisons, Lee and Park's algorithm is shown numerically generally slightly better than the alternative algorithm due to the simple binary search scheme used. The alternative algorithm also requires fewer comparisons than that of the Guu algorithm. Furthermore, in comparison of the average CPU time requirements, the ranks of the results of these algorithms are consistent with those of the algorithms for the average numbers of calculations. In general, the alternative algorithm may provide an efficient alternative algorithm to the FWAs. In the worse-case situation, the Guu algorithm still may be considered as an efficient alternative.Index Terms--cut arithmetic, fuzzy arithmetic, fuzzy number, fuzzy weighted average (FWA).

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Application of fuzzy set theory and back-propagation neural networks in progressive die design

Cited by 23 publications

References 9 publications

Application of new fuzzy-weighted average (NFWA) method to engineering design evaluation

Application of new fuzzy-weighted average (NFWA) method to engineering design evaluation

An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making

A Comparison of Discrete Algorithms for Fuzzy Weighted Average

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