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Prediction of Secondary Dendrite Arm Spacing in Squeeze Casting Using Fuzzy Logic Based Approaches

Patel M. G. C., Krishna P., Parappagoudar M. B.

Archives of Foundry Engineering

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2015

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Vol. 15, iss. 1

51--68

EN

The quality of the squeeze castings is significantly affected by secondary dendrite arm spacing, which is influenced by squeeze cast input parameters. The relationships of secondary dendrite arm spacing with the input parameters, namely time delay, pressure duration, squeeze pressure, pouring and die temperatures are complex in nature. The present research work focuses on the development of input-output relationships using fuzzy logic approach. In fuzzy logic approach, squeeze cast process variables are expressed as a function of input parameters and secondary dendrite arm spacing is expressed as an output parameter. It is important to note that two fuzzy logic based approaches have been developed for the said problem. The first approach deals with the manually constructed mamdani based fuzzy system and the second approach deals with automatic evolution of the Takagi and Sugeno’s fuzzy system. It is important to note that the performance of the developed models is tested for both linear and non-linear type membership functions. In addition the developed models were compared with the ten test cases which are different from those of training data. The developed fuzzy systems eliminates the need of a number of trials in selection of most influential squeeze cast process parameters. This will reduce time and cost of trial experimentations. The results showed that, all the developed models can be effectively used for making prediction. Further, the present research work will help foundrymen to select parameters in squeeze casting to obtain the desired quality casting without much of time and resource consuming.

2

Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

Kumar D. T., Soleimani H., Kannan G.

International Journal of Applied Mathematics and Computer Science

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2014

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Vol. 24, no. 3

669--682

EN

Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network.

3

Neuro-fuzzy approach to the next day load curve forecasting

Dudek G.

Przegląd Elektrotechniczny

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2011

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R. 87, nr 2

61-64

EN

An adaptive neuro-fuzzy inference system ANFIS is used to the short-term load forecasting. ANFIS combines the comprehensibility of fuzzy rules and the adaptability and self-learning algorithms of neural networks. The model maps the input pattern of the sequence of the historical hourly load time series to the component of the next sequence. Input space is divided on fuzzy sets by fuzzy c-means clustering. The most informative input variables are determined using deterministic variable selection algorithms. Individual models are constructed for each day type and hour of the day. The method is applied to several load forecasting problems.

PL

Adaptacyjny neuronowo-rozmyty system wnioskujący ANFIS zastosowano do prognozowania krótkoterminowego obciążeń systemów elektroenergetycznych. ANFIS łączy czytelność reguł rozmytych i adaptacyjność samouczących się sieci neuronowych. Model odwzorowuje obraz wejściowy sekwencji historycznego godzinowego szeregu czasowego obciążeń na składową obrazu następnej sekwencji. Przestrzeń wejściowa jest dzielona na zbiory rozmyte przy użyciu rozmytej metody c-średnich. Zmienne wejściowe niosące najwięcej informacji wyznaczane są za pomocą deterministycznych algorytmów selekcji zmiennych. Odrębne modele są tworzone dla każdego typu dnia i godziny doby. Metodę zastosowano do kilku problemów prognozowania obciążeń.