Research on optimization of temperature measurement points for linear feed axis of CNC machine tools
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摘要: 为分析数控机床直线进给系统的温度场，需要布置一定数量的温度测点来进行温度数据的采集。然而，测点的位置和数目都会对研究结果产生直接影响。为实现准确布置温度测点，文章提出一种基于统计学理论进行改进的Canopy-FCM-GRA温度测点优化模型。以某数控机床X向直线进给轴为例，首先根据测得的实验数据确定预聚类数，然后通过模糊矩阵和灰色关联度系数筛选出相应的温度敏感点，最后基于SVR理论分别建立温度测点优化前和优化后的温度-热误差预测模型，通过比较两个模型精度来验证温度测点优化的有效性。结果表明，温度测点优化效果良好，通过优化后的测温点可准确探究进给系统的热特性。Abstract: To analyze the temperature field of the linear feed system of CNC machine tools, it is necessary to arrange a certain number of temperature measurement points for temperature data collection. However, the research results will be directly affected by the location and number of measurement points. To achieve accurate placement of temperature measurement points, an improved Canopy FCM-GRA temperature measurement point optimization model based on statistical theory is proposed in this paper. Taking the X-axis linear feed axis of a certain CNC machine tool as an example, the pre clustering number is first determined based on the measured experimental data, and then the corresponding temperature sensitive points are selected through fuzzy matrix and grey correlation coefficient. Finally, based on SVR theory, temperature thermal error prediction models are established before and after temperature measurement point optimization. The effectiveness of temperature measurement point optimization is verified by comparing the accuracy of the two models. The results show that the optimization effect of temperature measurement points is good, and the optimized temperature measurement points can accurately explore the thermal characteristics of the feed system.
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表 1 温度传感器布置说明
温度传感器编号 布置位置 T1、T10 远端导轨、电机端导轨 T2、T3 丝杠尾座、螺母座 T5、T9 电机端丝杠前/后 T6 电机外壳 T4、T7、T8 环境、工作台、床身 
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表 4 各温度测点的灰色关联度系数
温度测点 灰色关联度系数 温度测点 灰色关联度系数 T1 0.690 2 T6 0.749 6 T2 0.694 4 T7 0.693 0 T3 0.713 9 T8 0.690 9 T4 0.690 1 T9 0.704 7 T5 0.712 6 T10 0.701 4
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表 5 模型Ⅰ、Ⅱ的评价系数值
系数类型 模型I结果 模型II结果 MAE 0.375 9 0.426 MSE 0.297 4 0.306 6 RMSE 0.545 3 0.553 7 r2 0.975 3 0.976
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