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Fig. 1. Step casting geometry and location of samples are pictured.

• Mode, distribution and morphol- ogy of the graphite

• Austenitic growth • Segregation behavior of the alloy- ing elements

• Solid phase transition austenite • Ferrite/pearlite dependency on seg- regation conditions and diffusion distances

• The moving condensation zone in the mold sand. While the solidification phe-

nomena already are considered in CGI simulation, the team needed to develop a model for the ferrite/pearlite distribution. Te creation of ferrite and pearlite is driven by the graphite distribution established during the solidification and eutectoid transfor- mation. CGI solidification leads to two kinds of eutectic cells—ductile iron cells and CGI cells. Both grow according to unique mechanisms. Besides the chemical composition and inoculation, the cooling determines whether more sphaerolites (ductile

microstructure distributions using virtual and real thermocouples. Te microstructures were evaluated for the two variants of the step casting with final magnesium contents of 0.015% and 0.018% as well as selected locations in the engine block. Te locations within the crankcase cover areas of different wall thickness and


Procedure Te microstructure

prediction model was vali- dated through comparison of predicted and measured

iron cells) or compacted graphite will be present at the end. Researchers aimed to recreate typical

microstructure distributions in tensile bars to find a representative correlation between microstructure and fatigue values. Additional step castings also were poured. Te geometry of the step castings and the locations of the fatigue test samples are shown in Figure 1. Te melts poured were conditioned with two specific magnesium treatments. Te resulting nodularity values represent typical nodularity distributions found in critical areas of engine blocks. Te vari- ous wall thicknesses of the step casting provided an additional variation of the ferrite/pearlite ratio. Te fatigue experiments included

tension-compression as well as alternating bending loads. Because lifetime prediction analysis tools use tension and elonga- tion concepts to calculate fatigue values, the researchers conducted tension and elongation-regulated experiments. Te fracture surfaces were evalu-

cooling rates to provide samples with significantly differing microstructures. Te elevated magnesium led to a significant increase in the measured and simulated nodularity in the steps of the step casting. Te same higher nodularities were shown in the rapidly solidifying areas of the engine block versus the thick-walled bearing seg- ments. Generally, the measured and predicted nodularities matched well within the measured range (Fig. 3). Te step casting and crankcase

samples were used in numerous fatigue

Fig. 2. Image analysis was used to evaluate nodularity.

ated after the failure of the fatigue sample, and the local microstructure was determined using automatic image analysis (Fig. 2). Te researchers analyzed the number, shape and size of graphite particles, ferrite/pearlite ratio and chemical compositions.

test runs. Using the mathematical methods of variance and regression analysis, the researchers developed a correlation between local micro- structure and durability using the experimental data. Essential parts of the correlation function are the graphite shape and amount, as well as the pearlite fraction. No dominant microstructure parameter was found, so the correct combination of parame- ters is necessary to achieve the desired mechanical properties. Te accuracy of the model was determined to be 83%.

July 2013 MODERN CASTING | 37

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