基于新陈代谢原理的机床热误差伪滞后建模

为了建立预测精度高、泛化性能强的热误差预测模型,提出了一种基于新陈代谢原理的热误差伪滞后预测模型.通过实验研究发现了机床的伪滞后现象,并假设热误差是前一时刻关键点的温升及热误差共同作用的结果,求解出了机床的热关键点及典型工况下的热误差平均滞后时间.并利用遗传算法优化了最小二乘支持向量机的结构参数,基于新陈代谢原理对热误差进行迭代求解,从而建立了机床的热误差伪滞后预测模型.通过对比不同预测模型的预测结果,证明了假设的正确性,并且考虑伪滞后效应的预测模型的预测精度更高、泛化性能更好,能将不同转速的热误差降低90%以上.

Pseudo-hysteresis modeling for machine tool thermal error based on metabolic theory

In order to establish a thermal error prediction model with high prediction accuracy and generalization performance, a thermal error pseudo-hysteresis prediction model based on metabolic theory is proposed in this paper. The pseudo-hysteresis effect of machine tool is found by experimental research, and it is assumed that the thermal error is the result of the coupled action of the temperature rise at key points and the thermal error of the previous moment, and the thermal key points of the machine tool and the average lag time in the typical working conditions are solved. The genetic algorithm is used to optimize the structural parameters of the least squares support vector machine (LS-SVM). Based on the principle of metabolism, the thermal error is iteratively solved and the thermal error pseudo-hysteresis prediction model of the machine tool is established. The results of different prediction models show that the correctness of the hypothesis and the prediction accuracy of the pseudo-hysteresis prediction model is higher, the generalization performance is better, and the thermal error of different rotational speeds can be reduced by more than 90%.