Key Takeaways & Executive Findings
- •• Achieved a 31.863% reduction in average contact stress, a 70.5% reduction in matching wear work, and a 100.391% increase in rolling radius difference, significantly improving wheel-rail contact performance. • Proposed an optimal design method for rail grinding target profiles based on actual track and vehicle conditions, integrating wear profile measurement and grinding profile design. • Developed an RBF proxy model incorporating mechanical and geometric aspects of wheel-rail contact, enabling efficient simulation and optimization. • Utilized the NSGA-II algorithm to solve the multi-objective optimization model, demonstrating effectiveness in reducing costs and optimization time while enhancing rail grinding quality.
Abstract
This research aimed to overcome challenges such as high costs, lengthy optimization time, and low efficiency in resolving issues related to wheel-rail contact, rail wear, and vehicle dynamics. Based on the wheel-rail contact parameters, an optimal design method for rail grinding target profile is proposed from wear profile measurement to grinding profile design according to the actual railway track and vehicle operating conditions. We utilized Isight to create a simulation test and developed an RBF proxy model that incorporated both mechanical and geometric aspects of wheel-rail contact. By integrating rail modeling, wheel-rail contact analysis, and multi-objective optimization, we established a rail grinding optimization model that was solved using the NSGA-II algorithm. After optimization, the study achieved a 31.863% reduction in average contact stress, a 70.5% reduction in matching wear work, and a 100.391% increase in the difference in rolling radius between the wheel and rail.
1. Introduction
As one of the main basic components of rail transit system, the performance of steel rails directly affects the operation quality of the system. However, rail wear will deteriorate the wheel-rail contact relationship, resulting in significant vibration noise and component failure, which will have a direct impact on driving safety, operating economy, and ride comfort [1–5]. Nowadays, one of the most efficient technical solutions to the wear issue is rail grinding [6–11]. The service life of important components, such as rails and wheels, can be significantly extended, the running noise can be decreased, and the overall maintenance cost can be reduced by carrying out targeted grinding of the rails. By doing so, the development of wear can be controlled to the greatest extent, the matching relationship between wheels and rails can be improved, and rolling contact fatigue can be suppressed.
The design of grinding target profile directly affects the quality of grinding operation, which is one of the most critical links [12]. Many scholars have conducted extensive research on rail grinding profile optimization methods. Andrey et al. [13] used the method of calculating the geometric parameters of the rail cross-section to achieve data informationization of the rail grinding process, which significantly reduced the amount of rail grinding. Li et al. [14] studied the multi-objective optimization of rail profiles for high-speed rail with small radius curves, focusing on reducing rail wear and improving curve performance using a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) based method. A method for optimizing the profile of steel rails using a coupling of Artificial Neural Network and Genetic Algorithm (ANN-GA) was proposed by Jiang et al. [15] to prevent wear and tear on high-speed railway tracks.
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Zhiwei Wu, Wengang Fan, Jiang Li, Zhiao Liu, Jiating Yang (2025). Multi-Objective Optimization Approach for Achieving Target Profile in Rail Grinding of Worn Rails. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01208-8
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Frequently Asked Questions
What is the main objective of this research?
The main objective is to overcome challenges such as high costs, lengthy optimization time, and low efficiency in resolving wheel-rail contact, rail wear, and vehicle dynamics issues by proposing an optimal design method for rail grinding target profiles.
What optimization algorithm is used in this study?
The study uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve the multi-objective optimization model for rail grinding profiles.
What are the key improvements achieved after optimization?
After optimization, the study achieved a 31.863% reduction in average contact stress, a 70.5% reduction in matching wear work, and a 100.391% increase in the difference in rolling radius between the wheel and rail.
How is the RBF proxy model used in this research?
The RBF proxy model is developed to incorporate both mechanical and geometric aspects of wheel-rail contact, enabling efficient simulation and optimization by reducing computational time.
What is the significance of this research for railway maintenance?
This research provides a systematic approach to design rail grinding target profiles that improve wheel-rail contact, reduce wear, and extend component life, thereby lowering maintenance costs and enhancing operational safety and comfort.
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