数控机床力-几何误差的PSO-SVM建模

为了提高数控机床几何误差建模精度，改进补偿效果，先用测力环等仪器模拟施加并测量机床主切削力，再用激光干涉仪同步测量机床俯仰角和偏摆角误差. 根据粒子群优化算法(particle swarm optimization,PSO)优化支持向量机(support vector machine, SVM)的相应参数，并以实际测量数据进行训练，从而建立
了PSO-SVM力-几何误差预测模型. 实际试验表明，PSO-SVM误差预测模型输出的偏摆角误差预测值与实测数据的最大差值仅为0.6 μrad，俯仰角误差预测值与实测数据的最大差值仅为0.21 μrad，远小于利用BP神经网络以及常规方法优化的SVM所建立的力-几何误差预测模型的误差，因此该模型可用于数控机床几何误差的高精度实时补偿.

Force-Geometric Error Modeling of CNC Machine Tools Using PSO-SVM

To improve modeling precision of CNC geometric error and error compensation, the main cutting force is simulated and measured using a dynamometer. Errors in the pitching and deflection angles are measured with a laser interferometer. Trained with practically measured error data, the force-geometric error
predicting model based on PSO-SVM is established with the key parameters optimized using the particle swarm optimization algorithm (PSO). Verification experiments show that difference between the measured error and the maximum deflection angle error using the PSO-SVM model is 0.6 μrad, and that with the maximum error of pitching angle error is 0.21 μrad. Compared with the force-geometric error predicting model based on BP neural networks and SVM whose parameters is optimized using a conventional method, the prediction precision of the PSO-SVM error model is greatly improved. Therefore the proposed model can compensate geometric error of CNC machine tools in real-time with high-precision.