基于优化最小二乘支持向量机的数控机床热误差建模分析

数控机床热误差是降低加工精度的主要因素之一.针对热误差建模问题, 结合布谷鸟算法的随机莱维飞行机制和最小二乘支持向量机结构风险最小化与线性规划等优点, 提出基于布谷鸟算法优化最小二乘支持向量机的热误差建模方法.在最小二乘支持向量机将低维非线性问题转化为高维线性问题时, 构建了混合核函数.同时,采用布谷鸟算法对最小二乘支持向量机惩罚因子γ、核宽度参数σ和混合核权值λ进行了优化.以GMC2000A机床为实验对象, 分别对热误差数据进行了聚类分析和建模分析.通过误差预测对比分析得出结论, 基于布谷鸟算法优化混合核最小二乘支持向量机建立的误差模型取得了良好的预测效果, 且明显优于BP神经网络模型和未优化的最小二乘支持向量机模型的预测效果.

Thermal error modeling analysis of CNC machine tool based on optimized least squares support vector machine

The thermal error of CNC machine tools is one of the main factors that reduce the accuracy of the machining operation. For the establishment of the thermal error model, combined with the cuckoo algorithm random Levi flight mechanism, the least squares support vector machine structure risk minimization and linear programming advantages, a thermal error modeling method is proposed based on the least squares support vector machine which optimized by cuckoo algorithm. When the least squares support vector machine transforms the low-dimensional nonlinear problem into the high-dimensional linear problem, a hybrid kernel function is constructed, and the cuckoo algorithm is used. The penalty factor γ, the kernel width parameter σ and the mixed kernel weight λ of the least squares support vector machine is optimized by cuckoo algorithm. Taking the GMC2000A machine tool as the experimental object, clustering analysis and modeling analysis of the thermal error data are carried out. The prediction comparison analysis concluded that the error model established based on the cuckoo algorithm to optimize the hybrid kernel least squares support vector machine has achieved good prediction results, and is significantly better than the predictive effect of BP neural network model and the unoptimized least squares support vector machine model.