摘要
医用氧化锆陶瓷(Y-TZP)是较好的齿科修复体材料,为了得到较好的齿科修复体性能对于其制造精度特别是表面粗糙度的要求比较高,但其是硬脆难加工材料,为了提高医用氧化锆陶瓷磨削加工表面质量和加工效率,在对医用氧化锆陶瓷磨削过程中的声发射信号分频段进行相关性分析的基础上,提取磨削声发射840~850kHz敏感频段信号中与磨削表面粗糙度强相关的12组特征值,构建了具有较高预测精度的随机森林神经网络,最终医用氧化锆陶瓷磨削表面粗糙度声发射预测最大相对误差低于8.37%,研究结果对医用氧化锆陶瓷磨削表面粗糙度在线智能监测有较大的参考价值。
Medical zirconia ceramic(Y-TZP)is a good dental restoration material.To obtain good dental restoration performance,high manufacturing accuracy,especially surface roughness,is required.However,it is a hard,brittle material,which is difficult to machine.To improve the surface quality and processing efficiency of medical zirconia ceramic grinding,correlation analysis is conducted on the frequency bands of acoustic emission signals during the grinding process of medical zirconia ceramic.Twelve sets of characteristic values strongly related to grinding surface roughness in the sensitive frequency band signals of 840—850 kHz are extracted,and a random forest neural network with high prediction accuracy is constructed.Finally,medical zirconia ceramic grinding surface roughness is obtained.The maximum relative error of acoustic emission prediction is less than 8.37%,and the research results have great reference value for intelligent online monitoring of surface roughness in medical zirconia ceramic grinding.
作者
李波
郭力
LI Bo;GUO Li(College of Electrical and Information Engineering,Hunan University,Changsha 410082,China;College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China)
出处
《南京航空航天大学学报》
CAS
CSCD
北大核心
2024年第3期571-576,共6页
Journal of Nanjing University of Aeronautics & Astronautics
关键词
医用氧化锆陶瓷
磨削声发射
表面粗糙度预测
随机森林神经网络
相关性系数
medical zirconia ceramic
grinding acoustic emission
surface roughness prediction
random forest neural network
correlation analysis
