【摘要】 针对高速铁路构架横向加速度超限报警问题，通过调查车轮廓形与钢轨廓形，优化钢轨打磨目标廓形，构建并现场应用智能打磨模型以指导施工。对报警区段的调研显示，受轮轨磨耗影响，该区段钢轨廓形与全新60N廓形偏差显著，不符合验收标准，且轮轨接触名义等效锥度偏大。为减少打磨深度并改善轮轨关系，基于粒子群优化-非均匀有理B样条(PSO-NURBS)理论、反向传播-非支配排序遗传算法Ⅲ(BP-NSGAⅢ),结合车辆-轨道耦合动力学模型，求解得到打磨目标优化廓形；进一步选取优化廓形的打磨点及对应角度，结合现场需求生成64种变功率打磨方案及相应打磨深度。基于Matlab建立钢轨智能打磨模型计算并输出优化方案，现场应用后检测表明：打磨后钢轨名义等效锥度变化趋于平缓，与全新60N廓形接近；车辆-轨道耦合动力学仿真验证显示，打磨后车辆的最大构架横向加速度、最大脱轨系数、最大磨耗功较打磨前分别降低37.22%、26.90%、21.25%。钢轨智能打磨模型可有效提升打磨效率，改善轮轨关系，优化车辆运行的稳定性、安全性及轮轨磨耗特性。更多还原

【Abstract】 To mitigate excessive lateral acceleration alarms in high-speed railway bogies, this study analyzed wheel and rail profiles, optimized target rail grinding profiles, and constructed an intelligent grinding model for on-site application. Analysis of alarm sections shows that, affected by wheel-rail wear, the rail profiles in these sections significantly deviate from the new 60N profile, failing to meet the acceptance criteria, and resulting in elevated nominal equivalent conicity. An optimized target grinding profile was derived to minimize grinding depth and enhance wheel-rail interaction, leveraging Particle Swarm Optimization-Non-Uniform Rational B-Spline(PSO-NURBS) theory and Back Propagation-Non-Dominated Sorting Genetic Algorithm Ⅲ(BP-NSGAⅢ), integrated with a vehicle-track coupled dynamics model. Subsequently, grinding points and corresponding angles for the optimized profile were selected to generate 64 variable-power grinding schemes tailored to on-site requirements. A Matlab-based intelligent grinding model was developed to calculate and output optimized schemes. Post-grinding inspections show that, after grinding, the change in the nominal equivalent conicity of the rail tends to be gentler and more stable, approaching pristine 60N profile levels. Vehicle-track coupled dynamics simulations confirm reductions of 37.22%, 26.90%, and 21.25% in maximum bogie lateral acceleration, derailment coefficient, and wear work, respectively, compared to pre-grinding conditions. Overall, the proposed model significantly enhances grinding efficiency, optimizes wheel-rail interaction, and improves vehicle operational stability, safety, and wear performance.更多还原