机床主轴热关键点选择与典型转速热误差预测

doi:10.13229/j.cnki.jdxbgxb20210610

Abstract

Abstract:

Aiming at the key problem of the influence of thermal error on the static accuracy of machine tool spindle， a thermal error prediction model based on modified chicken swarm optimization algorithm （MCSO） and support vector machine （SVM） is proposed. Spectral clustering based on unsupervised learning and Spearman correlation analysis are used to identify the key sensitive temperature measuring points of the spindle， so as to reduce the number dependence of temperature data distribution and weaken the multicollinearity among temperature variables. Levy flight strategy is adopted in the local search of hens， and a nonlinear dynamic adaptive inertia weight for updating chick strategy is constructed to realize the global optimization of kernel function， and then MCSO is used to optimize the kernel function， penalty factor and deviation of SVM. MCSO-SVM， BP-GA， GA-SVM and CSO-SVM are used to establish thermal error models respectively. Meanwhile， the prediction ability of models under different working conditions is compared and analyzed. The experimental results of thermal error show that spectral clustering and Spearman correlation analysis can effectively reduce the coupling effect caused by collinearity of temperature variables； MCSO-SVM can achieve high-precision prediction of spindle thermal errors under typical speed， and the model has good generalization ability and robustness.

Key words: machine tool, thermal error, spectral clustering, modified chicken colony algorithm, support vector machine

CLC Number:

TH161

Shi-jie GUO,Xue-wei ZHANG,Nan ZHANG,Guan QIAO,Shu-feng TANG. Thermal key point select and error prediction under typical speed of machine tool spindle[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 72-81.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

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Table 1

Fig.7

Table 2

Comparison of goodness of fit of thermal error"

模型	参数	指标
模型	参数	\|e_i \|_min	\|e_i \|_max	\|e_i \|	RMSE	R²	η
BP?GA	E $x$	7.7239	0.2584	3.05465	3.7329	0.9497	87.81
	θ_x	0.0133	0.0017	0.0077	0.0085	0.8390	82.59
	θ_y	0.0126	0.0027	0.0067	0.0073	0.9519	89.26
CSO?SVM	E $x$	5.8655	0.6151	1.844658	2.1517	0.9793	92.97
	θ_x	0.0106	0.0017	0.00541	0.0061	0.9130	89.35
	θ_y	0.0132	0.0015	0.005142	0.0060	0.9692	93.17
MCSO?SVM	E $x$	0.4898	2.9588	1.1878	1.3016	0.9907	94.33
	θ_x	0.0001	0.0075	0.0038	0.0043	0.9670	91.64
	θ_y	0.0002	0.0045	0.00257	0.0027	0.9932	94.91
GA?SVM	E $x$	1.5278	7.7445	4.0326	4.3426	0.8938	81.05
	θ_x	0.0004	0.0132	0.0053	0.0063	0.9310	90.27
	θ_y	0.0011	0.0121	0.0056	0.0062	0.9654	92.11

Table 2

Fig.8

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测点	聚类组	相关系数	测点	聚类组	相关系数
T₁	1	0.9356	T₆	2	0.9519
T₂	4	0.9875	T₇	3	0.9547
T₃	4	0.9399	T₈	2	0.9717
T₄	4	0.8453	T₉	4	0.9356
T₅	5	0.9450	T₁₀	2	0.8865

Thermal key point select and error prediction under typical speed of machine tool spindle

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