基于多目标优化的微铣削表面粗糙度检测指标研究

Research on Surface Roughness Detection Index of Micro-Milling Based on Multi-Objective Optimization

针对微铣削表面形貌复杂而难以准确评价的问题,提出了一种基于三维表征的表面粗糙度检测指标。首先,在主成分分析的基础上,基于灰色关联分析方法将表征意义不同的三维表面粗糙度参数Sa、Ssk和Sku转化为单一的灰色关联度作为表面粗糙度检测指标;其次,利用响应曲面法建立灰色关联度模型,分析工艺参数对灰色关联度的影响规律;最后,获取最优工艺参数组合并进行验证。结果表明,灰色关联度模型的平均相对误差为6.54%,拟合精度高,预测效果好,验证了模型的有效性;获取的最优工艺参数组合对应的灰色关联度提高了15.27%,实现了表面粗糙度和表面异常特征最小化的目的,证明了检测指标的可行性。

Since the fact that the surface topography of micro-milling is complex and difficult to evaluate accurately,a surface roughnessdetection index based on three-dimensional(3D)characterization is proposed.Firstly,on the basis of principal component analysis,the 3D surface roughness parameters Sa,Ssk and Sku are converted into gray correlation degree as a surface roughness detection index based on the gray correlation analysis method.Secondly,the response surface methodology(RSM)is used to establish a gray correlation degree model to analyze the influence of machining parameters on the GRG.Finally,the combination of optimal machining parameters is obtained and verified.The results show that the average relative error of the gray correlation degree model is 6.54%,the fitting accuracy is high and the prediction effect is good,which verifies the feasibility of the model.The GRG corresponding to the obtained optimal process parameter combination is increased by 15.27%,which realizes the purpose of surface roughness minimization and surface abnormal features minimization and proves the feasibility of the detection index.