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Thermal Error Modeling of a Machine Tool Using Data Mining Scheme

机译:Thermal Error modeling of a machine Tool Using Data mining scheme

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摘要

In this paper the knowledge discovery technique is used to build an effective and transparent mathematic thermal error model for machine tools. Our proposed thermal error modeling methodology (called KRL) integrates the schemes of K-means theory (KM), rough-set theory (RS), and linear regression model (LR). First, to explore the machine tool's thermal behavior, an integrated system is designed to simultaneously measure the temperature ascents at selected characteristic points and the thermal deformations at spindle nose under suitable real machining conditions. Second, the obtained data are classified by the KM method, further reduced by the RS scheme, and a linear thermal error model is established by the LR technique. To evaluate the performance of our proposed model, an adaptive neural fuzzy inference system (ANFIS) thermal error model is introduced for comparison. Finally, a verification experiment is carried out and results reveal that the proposed KRL model is effective in predicting thermal behavior in machine tools. Our proposed KRL model is transparent, easily understood by users, and can be easily programmed or modified for different machining conditions.
机译:在本文中,知识发现技术用于建立有效且透明的机床数学热误差模型。我们提出的热误差建模方法(称为KRL)整合了K-均值理论(KM),粗糙集理论(RS)和线性回归模型(LR)的方案。首先,为了探索机床的热行为,设计了一个集成系统,以在合适的实际加工条件下同时测量选定特征点的温度上升和主轴鼻端的热变形。其次,将获得的数据通过KM方法分类,再通过RS方案进一步简化,并通过LR技术建立线性热误差模型。为了评估我们提出的模型的性能,引入了自适应神经模糊推理系统(ANFIS)热误差模型进行比较。最后,进行了验证实验,结果表明所提出的KRL模型可有效预测机床的热行为。我们提出的KRL模型是透明的,易于用户理解,并且可以针对不同的加工条件轻松进行编程或修改。

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