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Abstract
Wheel tread wear is a form of wheel damage that can seriously affect the performance of freight vehicles. A new numerical approach to optimizing wheel profiles can reduce circular wear on the LM wheel in the design cycle. This approach considers the influence of different line conditions and speed fluctuation on wheel wear, along with the performance of the wheel and the rail as the materials wear. In this approach, a nonlinear numerical optimization model for the wheel tread profile is built through a backpropagation (BP) neural network method. The multipoint Kik–Piotrowski (KP) contact mechanics model is applied to calculate the wheel/rail normal force, tangential creep force, the stick-slip area, and the size and shape of the contact patch. The optimal profile is obtained through the genetic algorithm (GA) method. In order to better reflect the random characteristics of wheel/rail matching and interval uncertainty, a random sampling technique is used to generate a random data sample at typical operating speeds.
Highlights
On high-speed and heavy-haul railways, wheel/rail wear problems have become serious, especially in terms of circular wheel wear and hollow-tread wear [1]
Evaluation of how effectively wheel and rail profiles are optimized is currently based on wheel and rail wear and the dynamic vehicle performance based on the as-designed profiles, without taking the effects of wear into account, when in reality, the wheel and rail shapes and the actual wheel/rail contact are constantly changing with wear [15]
E wheel profile shape satisfies the geometric characteristic, and the vehicles’ dynamic performance satisfies operational safety requirements. ese are defined as the constraint condition. e wheel profile optimization model is established by a neural network method, and the optimal profile is calculated using a genetic algorithm (GA) [17, 18]
Summary
On high-speed and heavy-haul railways, wheel/rail wear problems have become serious, especially in terms of circular wheel wear and hollow-tread wear [1]. An effective wheel/rail profile design incorporates a tapered wheel tread to generate the rolling radius difference (RRD), which promotes steering in curves and has a significant influence on the dynamic vehicle performance, overall [6]. Evaluation of how effectively wheel and rail profiles are optimized is currently based on wheel and rail wear and the dynamic vehicle performance based on the as-designed profiles, without taking the effects of wear into account, when in reality, the wheel and rail shapes and the actual wheel/rail contact are constantly changing with wear [15]. Erefore, a new approach to wheel profile design that takes normal wear characteristics into account is proposed In this approach, simulation and analysis are based on actual line and speed-related operating conditions. E wheel profile shape satisfies the geometric characteristic, and the vehicles’ dynamic performance satisfies operational safety requirements. ese are defined as the constraint condition. e wheel profile optimization model is established by a neural network method, and the optimal profile is calculated using a genetic algorithm (GA) [17, 18]
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