Physical simulation of investment casting for GTD-222 Ni-based superalloy processed by controlled cooling rates
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The influence of the solidification process parameters on the microstructure is still identified by the trial and error method. It is common practice to perform multiple casting tests to defecate the optimum process parameters for high-quality casting parts. In order to establish the solidification-microstructure relationship, a high-efficiency experimental method is proposed to accelerate the speed of finding the optimum casting process parameters by the controlled cooling rate of 0.25 °C/s, 1 °C/s, 5 °C/s, and 10 °C/s, respectively. It demonstrated that the physics simulation can successfully predict the microstructure of the GTD-222 Ni-based superalloy casting and the relationship between the secondary dendrite arm spacing (SDAS) and cooling rate is λ2 = 76.4747(GV)−0.2926.The response behavior of secondary dendrite arm spacing is sensitive to the change of solidification parameters. Moreover, the microhardness tends to decrease along the axial direction as well. The relationships between the temperature gradient, cooling rate, and microstructure are discussed as well. The results also show that the prior model of the numerical simulation and the physical simulation of the high-efficiency experiment design can reproduce the conventional casting conditions and the high-efficiency experiment can be applied to other casting studies of all kinds for the enhancement of time- and cost-saving.
KeywordsGTD-222 Ni-based superalloy Microstructure cooling rate Temperature gradient High-efficiency experiment
This work was financially supported by The Major State Basic Research Development Program of China (2016YFB0701405) and National Natural Science Foundation of China (51705314, 51771118, 51821001, U1760110). The authors gratefully acknowledge the financial supports from the National Industrial Basis Improvement Project under Project (TC160A310-12-1), The 13th Five-year Major Project of Aero Engine and Gas Turbine of China (2017-VII-008) and Startup Fund for Youngman Research at SJTU (18X100040025).
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