The Probabilistic Study of Heat Treatment Process for Railroad Wheels Using ANSYS PDS

This paper outlines a probabilistic analysis performed on a Griffin Wheel CJ36 freight car wheel design to demonstrate how ANSYS Probabilistic Design System (PDS) can be used to understand the production process uncertainties and the parameter variations in a manufacturing process. Compared to the traditional deterministic approach, the probabilistic method is a more reliable means to account for uncertainties, when optimizing manufacturing processes as well as the product designs. The ANSYS/PDS provides an efficient tool to assess the interactions, effects, and sensitivities between input parameters and output variability. After casting or forging, railroad wheels are heat-treated to induce the desirable circumferential compressive residual stress in the upper rim. However, the heat treatment process also generates an axial tensile stress that could contribute to Vertical Split Rims, which is a catastrophic wheel failure mode. To investigate the effects of different parameters in the heat treatment process and to identify those that have the greatest impact on the residual stress field of the railroad wheels, a probabilistic study was conducted by using the ANSYS/PDS tool. The results revealed that residual stress is highly sensitive to the creep material property. Therefore, this factor significantly affects the residual stress field of railroad wheels during heat treatment.
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