Hardness prediction of Nodular Cast Iron Based on magma simulation software

Nodular cast iron is a widely used cast iron material at present. Because of its good mechanical properties, relatively simple process and low manufacturing cost, it is widely used in many fields such as automobile. In recent years, with the improvement of simulation accuracy, a series of casting simulation analysis software has been rapidly popularized in the industry and recognized by more and more casting enterprises. It not only improves the production efficiency, but also greatly reduces the R & D cost and cycle, adding new vitality to the traditional casting industry. Taking magma software as an example, it can mainly realize fluid flow simulation in nodular cast iron module, which is used to analyze defects such as sand washing, porosity, cold shut and so on; Heat transfer solidification simulation is used to analyze casting shrinkage porosity, shrinkage cavity and other defects; Performance and microstructure prediction, which is used to analyze and predict mechanical properties and metallographic hardness; And stress-strain prediction, which is used to analyze defects such as deformation and crack.

According to the actual use, its application in fluid flow and heat transfer solidification is relatively mature, but there are still some differences between the prediction of performance structure and stress-strain and the actual production situation. The author mainly analyzes the hardness prediction of nodular cast iron through the numerical simulation analysis of magma software and the actual production results, and provides an analysis idea.

The relationship function between hardness and cooling rate obtained by fitting nodular cast iron does not have universal applicability. The actual hardness value is not only related to eutectoid cooling rate, but also affected by chemical composition, process layout, casting shape and so on. Therefore, it is necessary to determine the fitting relationship function by measuring the hardness values at different positions on the premise that other change points are consistent as far as possible. However, the fitting method of regression curve is not unique, and the fitting method with high correlation can be selected as far as possible.

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