利用有限元法和数据挖掘技术作为确定圆锥滚子轴承最大承载能力的替代方法

Q1 Mathematics
Ruben Lostado-Lorza , Ruben Escribano-Garcia , Roberto Fernandez-Martinez , Marcos Illera-cueva , Bryan J. Mac Donald
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引用次数: 28

摘要

双列圆锥滚子轴承(trb)是设计用于支持预载荷,径向载荷,轴向载荷和扭矩组合的机械装置。广泛应用于高负荷、中等转速的车辆。这种载荷组合在轴承滚道上产生难以计算的高接触应力,并可能导致疲劳剥落和点蚀等不良缺陷。近几十年来,有限元法(FEM)已被用于获得每个滚道上的接触应力分布,尽管这种方法具有计算成本高的缺点。TRB上无数可能的输入载荷组合(预载荷、径向载荷、轴向载荷和扭矩)使得计算这些接触应力的分布变得更加困难。本文提出了一种结合有限元法和数据挖掘技术确定trb最大承载能力的方法。首先,根据实际材料的性质、几何形状和构成双排TRB的各部件的摩擦系数,建立了双排TRB的三维有限元模型;随后,完成了考虑上述输入载荷组合的实验设计(DoE),并在有限元模型中进行了模拟。基于有限元模拟得到的接触应力,建立了线性回归(LR)、高斯过程(GP)、人工神经网络(ANN)、支持向量机(SVM)和回归树(RT)等回归模型,预测了作用在TRB外滚道各排滚子上的接触应力比。最后,将基于遗传算法(GA)的进化优化技术应用于已有的最佳回归模型,实现输入负荷的最佳组合。当获得的径向载荷最大时,TRB的承载能力达到最大,而TRB外滚道两个触点的应力比接近25%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using the finite element method and data mining techniques as an alternative method to determine the maximum load capacity in tapered roller bearings

Double-row tapered roller bearings (TRBs) are mechanical devices designed to support a combination of preload, radial load, axial load and torque. They are widely used in vehicles for high load and moderate rotation speeds. This combination of loads produces high contact stresses on the bearing raceways that are difficult to calculate, and can cause undesirable defects like fatigue spalling and pitting. In recent decades, the Finite Element Method (FEM) has been used to obtain the distribution of the contact stresses on each of the raceways, although this method has the disadvantage of a high computational cost. The myriad of possible combinations of input loads on the TRB (preload, radial load, axial load and torque) makes it much harder to calculate the distribution of these contact stresses. This paper proposes a methodology that combines the FEM and data mining techniques to determine the maximum load capacity in TRBs. First, a three-dimensional finite element (FE) model was generated according to the real materials' properties, geometry and coefficients of friction of all parts that make up the double-row TRB. Subsequently, a Design of Experiment (DoE) was completed that considered a combination of the mentioned input loads, which were simulated in the FE model. Based on the contact stresses obtained from the FE simulations, a group of regression models – linear regression (LR), Gaussian processes (GP), artificial neural networks (ANN), support vector machines (SVM) and regression trees (RT) – were built to predict the contact stresses ratios that act on each of the row of rollers in the outer raceway of the TRB. Finally, the best combination of input loads was achieved by applying evolutionary optimization techniques based on genetic algorithms (GA) to the best regression models previously obtained. The maximum load capacity of the TRB was achieved when the radial load obtained was a maximum, while the stresses ratios of the two contacts in the outer raceway of the TRB were close to 25%.

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来源期刊
Journal of Applied Logic
Journal of Applied Logic COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
1.13
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation.
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