基于支持向量机的静压转台热误差补偿

在一般补偿器的硬件中无法运行MATLAB等第三方工具软件的代码，导致大多模型不能被应用于机床热误差的实际补偿.为了提高误差建模效率，降低对补偿系统硬件的要求，提出静压转台热误差实时补偿方法. 该补偿方法以支持向量机（SVM）为核心算法，分别使用鱼群算法和狼群算法对支持向量机的核心参数进行前期和后期优化，在保证预测精度的前提下提升建模效率. 通过离线训练MATLAB筛选出支持向量导入到开发的补偿软件中，利用用于过程控制的对象连接与嵌入（OPC）方式对热误差实施实时在线补偿. 与传统多元线性回归建模方式对比，可以看出该模型在精度和效率上均较优.补偿实验的结果表明，转台的轴向误差由原来最大为40 μm降低为约10 μm，转台的加工精度提高了75%，验证了所提出补偿方法的有效性.

Thermal error compensation of static pressure turntable based on support vector machine

The code of third-party tool software such as MATLAB cannot be run in the hardware of general compensators, which leads to the fact that most models cannot be applied to the actual compensation of the thermal error of machine tools. A real-time compensation method for thermal error of static-pressure turntable was proposed, in order to improve the efficiency of error modeling and reduce the hardware requirements of the compensation system. The compensation method is based on the support vector machine (SVM). The fish population algorithm and the wolf group algorithm are used to optimize the core parameters of SVM in advance and later, and the modeling efficiency is improved under the premise of ensuring prediction accuracy. By the offline training of MATLAB, the support vector is filtered and imported into the developed compensation software, and object linking and embedding for process control (OPC) is used to implement real-time online compensation for thermal errors. Compared with the traditional multiple linear regression modeling method, the proposed model is better in accuracy and efficiency. The results of compensation experiment showed that the axial error of the turntable was reduced from the original maximum of 40 μm to about 10 μm. The machining accuracy of the turntable was improved by 75%, which verifies the effectiveness of the proposed compensation method.