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A prediction method of rail grinding profile using non-uniform rational B-spline curves and Kriging model

一种基于非均匀有理 B 样条曲线和 Kriging 模型的钢轨打磨廓形预测方法

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Abstract

Non-uniform rational B-spline (NURBS) curves are combined with the Kriging model to present a prediction method of the rail grinding profile for a grinding train. As a worn rail profile is a free-form curve, the parameterized model of a rail profile is constructed by using the cubic NURBS curve. Taking the removed area of the rail profile cross-section by grinding as the calculation index of the grinding amount, the grinding amount calculation model of a grinding wheel is established based on the area integral formula of the cubic NURBS curve. To predict the grinding amount of a grinding wheel in different modes, a Kriging model of the grinding amount is constructed, taking the travel speed of a grinding train, the grinding angle and grinding pressure of a grinding wheel as the variables, and considering the grinding amount of a grinding wheel as the response. On this basis, the prediction method of the rail grinding profile is presented based on the order-forming mechanisms. The effectiveness of this method is verified based on a practical application.

摘要

铁路钢轨打磨可以改善车辆运行质量, 延长钢轨使用寿命。 为了保证钢轨打磨后可以获得预定的打磨目标廓形, 需要在打磨之前对钢轨打磨廓形进行预测, 为打磨施工中打磨模式的优选提供依据。 本文设计了一种基于 NURBS-Kriging 模型的钢轨打磨廓形预测方法, 实现了不同打磨模式下钢轨打磨廓形的预测。 具体包括: 考虑磨损钢轨断面廓形为自由曲线, 应用三次 NURBS 曲线构建了钢轨廓形的参数化模型; 基于三次 NURBS 曲线面积精算公式, 设计钢轨打磨量的计算模型; 为预测单个砂轮的打磨量, 将钢轨断面打磨面积作为打磨量衡量指标, 以打磨角度、 打磨压力和打磨速度三个打磨参数为变量, 以打磨面积为响应量, 构建单砂轮打磨量的 Kriging 模型; 根据钢轨打磨顺序成形原理, 提出基于 NURBS-Kriging 模型的钢轨打磨廓形预测方法, 并通过工程应用验证了该方法的有效性。

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Correspondence to Wei Zeng  (曾威).

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Foundation item: Project(51405516) supported by the National Natural Science Foundation of China; Project(2015JJ2168) supported by the Natural Science Foundation of Hunan Province, China

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Yang, Y., Qiu, Ws., Zeng, W. et al. A prediction method of rail grinding profile using non-uniform rational B-spline curves and Kriging model. J. Cent. South Univ. 25, 230–240 (2018). https://doi.org/10.1007/s11771-018-3732-9

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  • DOI: https://doi.org/10.1007/s11771-018-3732-9

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