基于CSO-SVM的数控机床主轴热误差建模

针对数控机床多热源所致的温升与主轴热误差之间复杂的非线性关系问题，提出一种鸡群优化（chicken swarm optimization, CSO）算法与支持向量机（support vector machines, SVM）相结合的主轴热误差预测模型（以下简称热误差模型）。以某精密数控机床的主轴单元为研究对象，采用五点法对其在空转状态下的轴向热变形进行测量，并借助热电偶传感器对机床的4个关键温度测点的温度进行采集。以SVM为理论基础，随机选取75%的数据样本进行训练，进而构建主轴热误差模型。其中，利用CSO算法优化SVM模型的惩罚参数c和核参数g，以提升热误差模型的预测能力及鲁棒性。以余下的25%的样本作为测试数据集，对所得热误差模型进行验证。利用CSO-SVM模型对不同工况下主轴的热误差进行预测，并将预测结果与测量结果进行对比。结果表明：当主轴转速为3 000 r/min时，CSO-SVM模型的平均预测精度高达97.32%，相较于多元线性回归模型和基于粒子群优化的SVM模型分别提升了6.53%和4.68%；当主轴转速为2 000， 4 000 r/min时，CSO-SVM模型的平均预测精度分别为92.53%、91.82%，表明该模型具有较高的预测能力和良好的鲁棒性。CSO-SVM模型具有较强的实用性和工程应用价值。

Spindle thermal error modeling of NC machine tool based onCSO-SVM

Aiming at the complex nonlinear relationship between temperature rise and spindle thermal error caused by multiple heat sources of numerical control (NC) machine tool, a spindle thermal error prediction model (hereinafter referred to as thermal error model) based on chicken swarm optimization (CSO) algorithm and support vector machine (SVM) was proposed. Taking the spindle unit of a precision NC machine tool as the research object, the axial thermal deformation under idle state was measured by five-point measurement method, and the temperatures of four key temperature measuring points of the machine tool were collected using thermocouple sensor. Based on SVM theory, 75% data samples were randomly selected for training, and then the spindle thermal error model was constructed. Among them, CSO algorithm was used to optimize the penalty parameter c and kernel parameter g of SVM model to improve the prediction ability and robustness of the thermal error model. The remaining 25% of the samples were used as the test data set to verify the thermal error model.The spindle thermal error under different working conditions was predicted using CSO-SVM model, and the predicted results were compared with the measured results.The results showed that when the spindle rotate speed was 3 000 r/min, the average prediction accuracy of CSO-SVM model was as high as 97.32%, which was 6.53% and 4.68% higher than that of multiple linear regression model and SVM model based on particle swarm optimization, respectively; when the spindle rotate speed was 2 000 and 4 000 r/min, the average prediction accuracy of CSO-SVM model was 92.53% and 91.82% respectively, indicating that the model had high prediction ability and good robustness. CSO-SVM model has strong practicability and engineering application value.