Measuring and Predicting Heavy-duty CNC Lathe Positioning Accuracy Using Neural Networks
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摘要: 为了能够准确快速地评估重型数控(CNC)车床的定位精度,对该型车床的定位精度能够有一个全面分析,因此对目前某型号数控车床进行了研究。在分析重型数控车床定位精度的激光测量原理和方法的基础上,选取Z轴方向三段位移,根据国标规定每一段位移上确定目标位置点7个,采用激光干涉仪对三段位置定位精度进行测量,得到目标位置点的定位精度。采用六次多项式对其中一段测量数据进行拟合,在拟合曲线上选取大量等距节点,提出基于全域数据的BP神经网络(BPNN)和RBF神经网络(RBFNN)对机床Z轴方向定位精度进行预测的方法。与实测结果对比,两种预测方法可行有效。Abstract: In order to accurately and quickly assess the positioning accuracy of a heavy-duty CNC lathe and carry out its comprehensive analysis,we study a certain heavy-duty CNC lathe.First,we study the principles of laser measurement of its positioning accuracy.Then we select the three-section displacement in the Z axis direction.According to China's standards,we determine seven goal position points for each section of displacement and measure the positioning accuracy of three sections with the laser interferometer,thus obtaining the position accuracy of goal position points.We fit the measured data of one of the sections with sixth order polynomial and select many equidistant nodes along the fitting curve.Finally we propose our method for predicting the positioning accuracy along the Z-axle direction of the heavy-duty CNC lathe,which is based on the full-domain data BP neural network and RBF neural network.The comparison of the measured results indicates that the prediction based on the two neural networks is feasible and effective.
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Key words:
- heavy-duty CNC lathe /
- positioning accuracy /
- laser measurement /
- neural network /
- accuracy prediction
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