基于模拟退火遗传算法优化BP网络的数控机床温度布点优化及热误差建模

CNC Machine Tool Temperature Measuring Point Optimization and Thermal Error Modeling Based on Stimulated Annealing and Optimized BP Network by Genetic Algorithm

在热误差建模中,温度测点的优化选择至关重要。提出了运用相关性方法,分析测点温度与主轴热漂移之间的关系,找到相关性较高的测点位置,实现温度布点的优化选择。在此基础上采用模拟退火遗传算法(GSA)优化BP神经网络的方法建立热误差模型,并通过实验验证。结果表明:优化的热误差模型能够跳出局部最优而达到全局最优解,得到的误差模型拟合值更加接近实测误差值;基于GSA优化的BP网络模型较传统的神经网络模型有较高的精度及更强鲁棒性。

Optimization selection of the temperature measuring point is crucial during thermal error modeling. A method using correlation analysis was present to analyze the relationship between the spindle thermal drift and point temperature of measurement. The temperature distribution points of the optimal choice were achieved by finding a higher correlation measuring point of location. On basis of this, by using simulated annealing and genetic algorithm （ GSA） optimized BP neural network method, the thermal error model was established, and was verified by experiments. The results show optimized thermal error model can escape from local optimal and achieve global optimal solution. The resulting error model can fit values more closer to the actual measured error values. Based on simulated an？nealing genetic algorithm （ GSA） optimization, BP neural network model has higher accuracy and greater robustness than that of the traditional neural network model.