Application of principal component algorithm in spindle thermal error modeling of CNC machine tools

Table 1. Position and function of temperature sensor
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Table 1. Position and function of temperature sensor
传感器安放位置作用
T1~T5 主轴前轴承测量电机发热
T7，T8 主轴外箱测量主轴发热
T6，T9 主轴电机测量主轴发热
T10 机床外壳测量环境温度

Table 2. Experimental parameters of each batch

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Table 2. Experimental parameters of each batch

批次	环境温度范围/℃	主轴转速/r·min^-1	进给速度/mm·min^-1
K1	3.81-6.00	2 000	1500
K2	2.94-4.94	4 000
K3	3.62-7.19	6 000
K4	9.00-10.93	2 000
K5	13.81-16.00	4 000
K6	12.94-14.94	6 000
K7	21.12-24.81	2 000
K8	28.68-33.75	4 000
K9	32.37-35.06	6 000

Table 3. Principal components and contribution rate of K1
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Table 3. Principal components and contribution rate of K1
$Z_{i}$ $Z_{1}$ $Z_{2}$ $Z_{3}$ $Z_{4}$ $Z_{5}$ $Z_{6}$ $Z_{7}$ $Z_{8}$ $Z_{9}$ $Z_{10}$
$V_{i}$ 98.5 1.35 0.11 0.02 0.01 0 0 0 0 0

Table 4. Selection results of TSPs of each experiment（PCA）
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Table 4. Selection results of TSPs of each experiment（PCA）
批次 K1 K2 K3 K4 K5 K6 K7 K8 K9
结果 1，7 1，7 1，7 1，7 1，10 1，10 1，8 8，10 1，8

Table 5. Selection results of TSPs of each experiment（FCGC）
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Table 5. Selection results of TSPs of each experiment（FCGC）
批次 K1 K2 K3 K4 K5 K6 K7 K8 K9
结果 1，10 1，10 1，10 1，8 1，10 1，10 1，8 1，10 1，10

Table 6. Model coefficient of MLR of each batch experiment
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Table 6. Model coefficient of MLR of each batch experiment
批次模型系数
$b_{0}$ $b_{1}$ $b_{2}$
K1 -3.67 6.06 -3.97
K2 -3.83 6.17 -4.82
……
K8 4.49 9.39 -11.14
K9 -3.09 8.05 -2.89

Table 7. Model coefficient of ridge regression of each batch experiment
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Table 7. Model coefficient of ridge regression of each batch experiment
批次模型系数
$b_{0}$ $b_{1}$ $b_{2}$
K1 6.258 1.811 1.773
K2 14.474 1.633 1.644
……
K8 6.656 2.244 2.348
K9 14.905 1.775 1.850

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Xin-yuan WEI, Yu-chen CHEN, En-ming MIAO, Xu-gang FENG, Qiao-sheng PAN. Application of principal component algorithm in spindle thermal error modeling of CNC machine tools[J]. Optics and Precision Engineering, 2021, 29(11): 2649

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