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Analiza efektywności wykorzystania kryteriów informacyjnych w prognozowaniu zapotrzebowania na części zamienne

100%

Rosienkiewicz M.

2013

tom nr 5

11-21

W artykule przedstawiono klasyczne metody prognozowania zapotrzebowania na części zamienne oraz nowy trend w tej dziedzinie - wykorzystanie jednej z metod sztucznej inteligencji - sztucznych sieci neuronowych SSN (Sztuczne Sieci Neuronowe; Artificial Neural Networks, ANN).

The paper presents a new approach to the spare parts forecasting issue - a method which combines regression modeling, information criteria and artificial neural networks ANN. The research presented in this article compares efficiency of classical methods with the artificial intelligence tool in the scope of spare parts forecasting. Artificial Neural Networks have been advocated as an alternative to traditional statistical forecasting methods. Classical methods, such as exponential smoothing or mean average, have been used for several decades in forecasting demand. However, many of these techniques may perform poorly when demand for an item is lumpy or intermittent. In the paper three concepts of using ANN in spare parts forecasting - micro, macro and hybrid - were described. The article presents also the variable selection issue, which is of a great importance in any model building.

Design of experiments approach in AZ31 powder selective laser melting process optimization

51%

Pawlak A. , Rosienkiewicz M. , Chlebus E.

tom Vol. 17, no. 1

9--18

Additive manufacturing (AM) technologies are used to produce objects from a wide variety of materials. Technologies that enable processing of metal engineering materials such as selective laser melting have become particularly important. The group of materials processed in this technology comprises stainless steel, CoCr, titanium and aluminum alloys and is continuously expanding to include new materials. The results of studies into AZ31 magnesium powder selective laser melting process optimization are presented in this paper. Optimization was aimed at minimizing the melted material porosity and determining the influence of and correlations between the analyzed process parameters. Design of experiments (DOE) approach was used, specifically full factorial design for three factors and rotatable design for two factors. Satisfactory porosity values were obtained in the <0.5% range. The results were used to prepare a mathematical model for the resulting porosity depending on the parameters used. Additionally, it was shown that information on linear energy density and scan velocity is not sufficient to describe the SLM process, and that it is necessary to provide detailed values of component parameters.