基于ANFIS的高速电主轴热误差建模研究

Thermal error modeling of high-speed motorized spindle based on ANFIS

为了减少热误差对电主轴加工精度的影响,需要建立电主轴的热误差补偿系统,而补偿系统的性能主要取决于热误差预测模型的准确性和模型输入的温度质量。为保证输入模型的温度质量,采用模糊C-均值聚类和灰色关联分析相结合的综合算法优化温度测点,将温度测点的数量由10降至3个,以某台电主轴为试验对象,以电主轴转速为7 000 r/min的温度变量为输入,热误差变量为输出,采用自适应神经模糊推理系统建立了电主轴的热误差预测模型,并以转速为5 000和9 000 r/min的实验数据作为验证,结果表明,建立的ANFIS热误差预测模型可以有效地预测电主轴的热误差,预测模型的残差小于1μm。最后,与误差反向传播神经网络进行对比,结果表明该预测模型具有更高的精度和抗干扰能力。

To reduce the influence of thermal error on the machining accuracy of the electric spindle, it is necessary to establish a thermal error compensation system for the electric spindle. Its performance mainly depends on the accuracy of the thermal error prediction model and the temperature quality of the model input. To ensure the temperature quality of the input model, a comprehensive algorithm that fuses fuzzy C-means clustering and gray correlation analysis is used to optimize the temperature measurement points. The number of temperature measurement points is reduced from 10 to 3. The main spindle of the electric spindle is the test object. The temperature variable of the electric spindle speed of 7 000 r/min is used as the input, and the thermal error variable is the output. The adaptive neural fuzzy inference system is used to establish the thermal error prediction model of the electric spindle. The experimental data of 5 000 and 9 000 r/min are used as evaluation. Experimental results show that the formulated ANFIS thermal error prediction model can effectively predict the thermal error of the electric spindle. The residual error of the prediction model is less than 1 μm. Finally, compared with the back propagation neural network, results show that the prediction model has higher accuracy and anti-interference ability.