CAJ | 학술논문

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。
재열오차건모중，온도측점적우화선택지관중요。제출료운용상관성방법，분석측점온도여주축열표이지간적관계，조도상관성교고적측점위치，실현온도포점적우화선택。재차기출상채용모의퇴화유전산법（ GSA）우화BP신경망락적방법건립열오차모형，병통과실험험증。결과표명：우화적열오차모형능구도출국부최우이체도전국최우해，득도적오차모형의합치경가접근실측오차치；기우GSA우화적BP망락모형교전통적신경망락모형유교고적정도급경강로봉성。
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.