基于结构-整机性能映射模型的机床薄弱件结构优化方法

Structure Optimization Method of Machine Tool Weak Part Based on Mapping Model between Structure and Whole Machine Performance

提出了一种基于结构-整机性能映射模型的机床薄弱件结构优化方法。首先,通过机床动静态特性分析确定薄弱结构部件。其次,提出基于扩展常数自组织选取椭圆基函数(Elliptical basis function,EBF)的结构-动静态性能映射元模型建模方法:对椭圆基函数神经网络进行改进,提出基于扩展常数自组织选取的EBF建模方法,通过扩展系数的自组织选取以确定不同椭圆基函数合理的参与度与重叠性,避免所有椭圆基函数图形偏平或偏尖而影响EBF建模精度;基于改进后的椭圆基函数神经网络构建薄弱件结构-整机动静态性能映射元模型。通过实例样本数据检验得到,所构建的机床实例映射元模型计算结果与实际值之间的误差检验复相关系数均在0. 995以上,说明了该结构-整机性能映射元模型构建方法的正确性。在此基础上,根据上述薄弱件结构-整机动静态性能映射关系,以整机动静态性能为评价指标,以薄弱结构部件为优化对象,基于多目标优化算法,实现面向机床整机性能的薄弱件结构优化。

A structure optimization method of machine tool weak part based on mapping model between structure and whole machine performance was proposed. In this method, firstly the structure weak component was determined by the dynamic and static characteristics analysis of machine tools. Secondly, the structure-performance mapping modeling method based on elliptical basis function (EBF) neural network, whose extended constant was selected adaptively, was proposed. In this section, the elliptical basis function neural networks was modified and improved, and the EBF modeling method based on self-adaptive extended constant was proposed. The self-organizing selection of expansion coefficients was used to determine the reasonable participation and overlap of different elliptic basis functions, and it can avoid all elliptical basis functions from too flatting or too slant effectively, which may affect the accuracy of EBF modeling. Then, the structure-performance mapping model based on improved elliptic basis function neural network was structured. Also the validity and correctness of the mapped model was verified based on the sample data: the correlation coefficients between actual values and calculation results from mapped model were all above 0.995. Thirdly, on the above basis, according to the physical mapping relation between structure and static/dynamic performance of the whole machine tool, considering the effect of boundary constraint of the whole assembly, by taking dynamic and static performances as evaluation, and choosing the structure of weak component as the optimization object, based on multi-objective optimization algorithm, the optimization of weak structure part and the whole dynamic performance of machine tool were realized finally. After optimization, the center point deformation of tool was reduced by 12.8%, the mass of structure part was reduced by 9.7%, while the first order natural frequency of the whole machine tool was increased by 6.9%.