The Core Question in Engineering—Feeding and Shrinkage Defects
The support of the feeding related layout of the casting is still one of the most important duties for casting process simula- tion. Depending on the alloy poured, different feeding be- haviors and self-feeding capabilities need to be considered to provide a defect free casting. Therefore, it is not enough to base the prediction of shrinkage defects solely on hot spots derived from temperature fields but also to be able to quanti- tatively predict them (Fig. 11). Solidification simulation had to be combined with density and mass transport calculations in order to evaluate the impact of the solidification morphol- ogy onto the feeding behavior, as well as to consider alloy dependent feeding ranges. This is accomplished through the description of temperature dependent thermophysical proper- ties. Even if simple criteria functions like the ones provided by Niy ama or Lee offer important clues on shrinkage defects and micro porosities, current developments go far beyond that approach. State-of-the-art simulation tools consider indepen- dent growth models for pores in combination with the interac- tion of pressures and gas concentrations (Fig. 12).
A Necessary Condition—Data
In the early period of casting process simulation, foundries very often asked the question, “Is there any data for my al- loys?” This question was important and correct, as approxi- mately 50% of the accuracy of a simulation is determined by the algorithms used and the remaining 50% can be attributed to the thermophysical data. The “classic” thermophysical properties (conductivity, specific heat capacity, specific en- ergy, latent heat and density) are now readily available for all typical alloys. All data has to be provided in a temperature dependent format, as the information can change dramatically from pouring temperature through the solidification range to room temperature. With the introduction of mold filling simu- lation, additional rheological data needed to be provided, i.e. the viscosity of the melt throughout the solidification interval.
The introduction of stress simulation has expanded the need for data regarding thermo-mechanical properties (tempera- ture dependent, elastic modulus, area contraction, tensile strength, thermoplastic properties, time-dependent fatigue properties, etc.). The prediction of segregation requires in- formation derived from phase diagrams, as well as distribu- tion and diffusion coefficients. The micromodeling of mi- crostructures and mechanical properties necessitates quan- titative information about the growth of phases, the impact of alloying elements, as well as the quantitative consider- ation of metallurgical melt treatments like inoculation and grain refinement. Currently, the focus of data acquisition has moved toward the consideration of external process part- ners. These include mold materials and casting process aids like sleeves, filters, and coatings where the suppliers of these products are required to provide these data.
Figure 11. Secondary shrinkage below risers is shown for a ductile iron ring casting. This confirms that a simple heat flow calculation is not sufficient, as it only shows a ring shaped temperature distribution in the center of the casting. Only the combination of local shrinking and expansion behavior leads to a correct defect prediction.
Figure 12. Examples displaying the accuracy of shrinkage prediction in gray and ductile iron castings, as well as a steel casting.
12 International Journal of Metalcasting/Spring 10
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