运用响应面法的Ti-6Al-4V ELI钛合金铣削表面粗糙度预测模型  

Prediction Model for Surface Roughness in Milling of Ti-6Al-4V ELI Titanium Alloy Using Response Surface Methodology

作　　者：孙庆贞 魏学涛 张涛 张磊 魏旭东[3] SUN Qingzhen;WEI Xuetao;ZHANG Tao;ZHANG Lei;WEI Xudong(Department of Mechanical and Electrical,China Coast Guard Academy,Ningbo 315801,Zhejiang,China;Zichai Machines Co.,Ltd.,China National Agricultural Development Group Co.,Ltd.,Zibo 255086,Shandong,China;The Ministry of Education Key Laboratory of Advanced Manufacturing and Intelligent Technology,Harbin University of Science and Technology,Harbin 150080,China)

机构地区：[1]中国人民武装警察部队海警学院机电管理系,浙江宁波315801 [2]中国农业发展集团有限公司淄柴机器有限公司,山东淄博255086 [3]哈尔滨理工大学先进制造智能化技术教育部重点实验室,哈尔滨150080

出　　处：《机械科学与技术》2024年第4期643-649,共7页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家重点研发计划项目(2022YFB4300704)。

摘　　要：本研究采用端面铣削方式对Ti-6Al-4V ELI(TC4)钛合金进行加工,结合工艺参数进行建模以预测工件的表面质量并确定最佳切削参数。为了实现对工件表面质量的精确预测,在三轴数控加工中心上进行了相应的试验。试验基于Box Behnken方法(BBD)进行了四因素和三水平的设计,减少了试验所需的数目。试验中选择切削深度、切削宽度、切削速度和每齿进给量作为输入参数,将每次试验所测量的表面粗糙度作为输出参数。最终采用响应面法(Response surface methodology,RSM)建立输入参数和输出参数之间的二次关系,并进行方差分析(Analysis of variance,ANOVA)以评估所建立的模型。同时,利用RSM进行优化分析,确定铣削参数以实现最小表面粗糙度。分析表明,使用RSM建立铣削参数与表面粗糙度的二次回归模型,其校正系数为96.16%,模型能够较好反应输入参数与表面粗糙度的映射关系,该方法能够提供可靠的用于输入参数限制内任何铣削条件的表面粗糙度预测。经优化分析和试验验证,所得铣削参数能够获得较小的表面粗糙度,可应用于实践生产中的工艺优化。In this paper,Ti-6Al-4V ELI(TC4)titanium alloy is machined by end face milling,and the surface quality of the workpiece is predicted and the best cutting parameter is determined by combining the processing parameters.In order to realize the accurate prediction of workpiece surface quality,the corresponding experiments were carried out in three-axis CNC machining center.Based on Box Behnken method(BBD),the experiment was designed with four factors and three levels to reduce the number of experiments.In the experiment,the cutting depth,cutting width,cutting speed and feed per tooth are selected as the input parameters,and the surface roughness measured in each experiment is taken as the output parameters.Finally,the Response Surface Methodology(RSM)is used to establish the quadratic relationship between the input and the output parameters,and the analysis of variance(ANOVA)is conducted to evaluate the established model.At the same time,RSM is used to optimize the analysis and determine the milling parameters to achieve the minimum surface roughness.The analysis shows that the quadratic regression model for milling parameters and surface roughness is established via RSM,and its correction coefficient reaches 96.16%.The model can well reflect the mapping relationship between the input parameters and the surface roughness,and this method can provide the reliable surface roughness prediction for any cutting conditions within the input parameters.Through optimization analysis and experimental verification,the obtained milling parameters can obtain the smaller surface roughness,which can be applied to optimize the process in practice.

关 键 词：响应面法 二次回归模型 表面粗糙度 方差分析 

分 类 号：TH161[机械工程—机械制造及自动化]