基于模糊逻辑推理的铣切削过程稳健参数优化

The Robust Optimization for Multiple Responses in End Milling Process Based on Fuzzy Logic Inference

研究利用模糊逻辑推理的方法对铣切削过程进行稳健参数优化.根据铣切削实验机理,引入可控因子和噪声因子,建立铣切削稳健参数优化模型,对切削表面粗糙程度和粗糙高度两个响应同时优化.首先对多响应进行降维,采用满意度法将两个响应值转化为满意度值,然后分别利用满意度函数法加权几何平均计算得到一个综合满意度值和采用模糊逻辑推理方法将两个响应的满意度值转化为一个模糊推理等级.最后对这两种方法的可控因子与输出值进行主效应分析,分析可得二者的优化结果具有高度一致性,验证了模糊逻辑推理方法在铣切削稳健参数优化过程的有效性.而模糊逻辑推理过程具有能够避免权重设置的不足,且不需要建立精确的数学模型等优点,也使得该方法在多响应稳健参数优化领域具有普适性.

The optimization method of end milling cutting process based on fuzzy logic inference is studied. Accordingto the mechanism of milling cutting,the control factors and noise factors are introduced,and the optimization millingmodel is constructed to optimize the surface roughness and height roughness simultaneously. Firstly,reducing thedimensions of multiple responses,desirability function method is used to turn the two response values to a desirabilityvalues,and then using the geometric average method to calculate a comprehensive desirability value and using fuzzylogic inference methods to calculate a fuzzy reasoning grade. Finally,according the the main effect chart analyzingthat two methods have a highly consistency optimization results. The fuzzy logic inference method to optimize themilling process is to be verified effective. And the fuzzy logic inference can avoid the shortcomings of the weighting,and not need to establish a precise mathematical model. The fuzzy logic inference can be widely used in the field ofrobust parameter optimization for multi-responses.