机床热补偿中温度变量分组优化建模

Grouping Optimization Modeling by Selection of Temperature Variables for the Thermal Error Compensation on Machine Tools

提出数控机床热误差分组优化建模。在数控机床热误差建模时 ,先根据温度变量之间的相关性对测量所得的所有温度变量进行分组 ,再根据各变量与热误差之间的相关性选择典型变量并加以组合 ,最后依据多元测定系数 (回归平方和与总平方和的比值 )确定用于建模的温度变量。给出了分组优化建模实例。通过分组优化建模 ,减少了选择温度变量和建模所需的时间 ,且避免了误差模型中的变量耦合 ,提高了热误差模型的精确性和鲁棒性 ,从而使数控机床热误差实时补偿更有效。

A grouping optimization modeling for the thermal errors of NC machine tools was presented. First, all of the temperature variables were grouped by the correlation between one another. Then, the representative ones which have the most strongly correlated relationship with the thermal errors of the machine were found from each group. Last, optimal combination of the temperature variables used in modeling was found by corresponding criteria. One modeling example was presented. The variable searching and modeling time is reduced greatly. In addition, the correlation grouping eliminates the coupling problems, so the robustness of the model can be increased and the predicting precision of the model with the optimal combination of the temperature variables is enhanced. The real time compensation will be more effective.