Multi-Objective Optimization of Lightweight Inboard Bearing Design for High-Speed Railway Axle

Abstract

This research delves into the intricate balance between reducing axle weight and maintaining structural integrity in high-speed rail transportation. Focusing on the critical factor of weight reduction in high-speed axle design, the study employs finite element simulations and standard calculations to systematically explore inboard and outboard bearing wheelsets. Particularly noteworthy is the examination of inboard bearing axles, revealing advantages in mass reduction, deflection, and stress mitigation, with an 8% lower weight than outboard bearing axles. Utilizing multi-objective optimization, the research achieves a remarkable 4% reduction in mass and an associated 4% decrease in stress, resulting in a 12% mass reduction compared to traditional axles. The study also enhances fatigue resistance, demonstrated through radial fatigue reverse factor (FRF) analysis. With a detailed methodology involving ABAQUS modeling, Python scripting, and optimization using the Pointer algorithm in Isight, this research adeptly navigates the trade-off, significantly contributing to the advancement of railway transportation systems.