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.

The efficiency of grinding experiments is an important issue for many industries. In this paper, a central composite design-based methodology was proposed to investigate the four design factors that affect the particle sizes. The four design factors were specified as mill speed (% of N_c), ball filling ratio (f_c), powder filling ratio (j_b) and grinding time (min). Another important issue was how to obtain an optimum operating condition for four design factors. For this particular purpose, a novel dual response optimization model was proposed using the particle sizes (d₁₀, d₅₀, and d₉₀) and the span value concept. This proposed approach was compared to the desirability function-based optimization concept. The verification study of the experiment was also carried out. The results of the grinding experiment runs showed that the optimum operating conditions were mill speed 73.495% of N_c, ball filling ratio 0.354, powder filling ratio 0.157, and grinding time 70 min. In addition, d₁₀, d₅₀, and d₉₀ were found 3.31 µm, 12µm, and 45.6 µm, respectively. The span value was also found at 3.52.