纵-扭超声磨削陶瓷表面粗糙度拟合与预测模型  

Fitting and Prediction Model of Ceramic Surface Roughness in Longitudinal-Torsional Ultrasonic Grinding

作　　者：陈友广[1] 聂佳梅[2] 马文举 CHEN Youguang;NIE Jiamei;MA Wenju(School of Intelligent Manufacturing,Suzhou Chien-Shiung Institute of Technology,Taicang Jiangsu 215411,China;School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang Jiangsu 212000,China;School of Mechatronics Engineering,Henan University of Science and Technology,Luoyang Henan 471003,China)

机构地区：[1]苏州健雄职业技术学院智能制造学院,江苏太仓215411 [2]江苏大学汽车与交通工程学院,江苏镇江212000 [3]河南科技大学机电工程学院,河南洛阳471003

出　　处：《机床与液压》2022年第22期74-79,共6页Machine Tool & Hydraulics

摘　　要：为探究纵-扭超声振动对陶瓷磨削表面几何形貌的影响,以ZrO_(2)陶瓷为研究对象,通过正交对比试验,以磨削表面粗糙度值为评价指标,采用多元线性回归分析法,建立普通磨削(OG)及纵-扭超声磨削(L-TUG)材料表面粗糙度拟合模型,研究工艺参数对表面粗糙度作用的主次顺序及影响程度;同时利用BP神经网络预测模型进行L-TUG表面粗糙度的优化求解。结果表明:在L-TUG中,主轴转速对粗糙度值影响最大,超声能量影响最小;在OG中,磨削深度对粗糙度值影响最大,主轴转速影响最小。BP神经网络模型预测误差在1.070%~9.396%内,且最优磨削参数组合获得的表面质量最好,可实现对L-TUG表面粗糙度值较高精度的智能预测。In order to investigate the effect of longitudinal-torsional ultrasonic vibration on the surface topography of ceramic materials grinding, taking ZrO_(2) ceramics as the research object and the grinding surface roughness as the evaluation index, multiple linear regression analysis method was used to establish the materials surface roughness fitting model of ordinary grinding(OG) and longitudinal-torsional ultrasonic grinding(L-TUG) through the orthogonal test, and the primary-secondary order and the influence degree of the process parameters on the surface roughness were studied.At the same time, BP neural network prediction model was used to optimize the surface roughness of L-TUG.The results show that: in L-TUG,the spindle speed has the greatest influence on the roughness value, the ultrasonic energy has the least influence;in OG,the grinding depth has the greatest influence on roughness value, while the spindle speed has the least influence.The prediction error of the BP neural network model is within the range of 1.070% to 9.396%,and the surface quality obtained by the optimal grinding parameter combination is the best, and the intelligent prediction to L-TUG surface roughness value with high accuracy can be realized.

关 键 词：纵-扭超声磨削 粗糙度 正交试验 BP神经网络 

分 类 号：TG506.5[金属学及工艺—金属切削加工及机床]