Determination of flow distance of the fluid metal due to fluidity in ductile iron casting by artificial neural networks approach

Abstract

Ductile irons (DIs) have properties such as high strength, ductility, and toughness, as well as a low degree of melting, good fluidity, and good machining. Having these characteristics make them the most preferred among cast irons. The combination of excellent properties, especially in DI castings with a thin section, make it an alternative for steel casting and forging. But in the manufacture of thin-section parts, fluidity characteristics need to be improved and the liquid metal must fill the mold completely. The fluidity of liquid metal is influenced by many factors depending on the casting processes such as material and mold properties, casting temperature, inoculation, globalization, and grain refinement. In this study, an artificial neural network (ANN) model has been developed that allows for determining the flow distance of the liquid metal in the sand mold casting method under changing casting conditions of DI. Thus, the flow distance was estimated depending on the cross-sectional thickness during the sand casting under changing casting conditions. The experimental parameters were determined as casting temperature, liquid metal metallurgy quality, cross-sectional thickness, and filling time. Filling modeling was performed with FlowCast software. When the results were examined, it was seen that the developed ANN model has high success in predicting the flow distances of the liquid metal under different casting conditions. The calculated coefficient of determination (R 2) value of 0.986 confirms the high prediction performance of the model.

Makine Öğrenmesi Yöntemleri ile Konut Talep Tahmini

“…In this study, each input is multiplied by the weight value (wij) that connects that input to the processing element and combined through the addition function given in Equation 1 [21]. Levenberg-Marquardt training algorithm was used because it is faster and more reliable than other training algorithms [22]. Figure 3 shows the cellular structure of the ANN [23].…”

Modeling the Effect of Pour Height, Casting Temperature and Die Preheating Temperature on the Fluidity of Different Section Thicknesses in Permanent Mold Casting of Al12Si Alloys

“…In fluidity tests known as spiral mold or vacuum test, the distance the liquid metal moves before solidifying is considered as a measure of fluidity [39,40]. Many studies on fluidity and casting simulation are available in the literature [41][42][43]. However, due to the fact that the section thickness is constant in these models, alternative fluidity test models are developed due to the differences observed in the results.…”