基于相关性分析与CNN-BiLSTM神经网络的PSZ陶瓷磨削表面粗糙度智能预测  

Intelligent Prediction of PSZ Ceramic Grinding Surface Roughness Based on Correlation Analysis and CNN‑BiLSTM Neural Network

作　　者：郭力[1] 郑良瑞 冯浪 GUO Li;ZHENG Liangrui;FENG Lang(College of Mechanical and Vehicle Engineering/National Engineering Research Center for High Efficiency Grinding,Hunan University,Changsha 410082,China)

机构地区：[1]湖南大学机械与运载工程学院/国家高效磨削工程技术研究中心,长沙410082

出　　处：《南京航空航天大学学报》2023年第3期401-409,共9页Journal of Nanjing University of Aeronautics & Astronautics

基　　金：湖南省自然科学基金科教联合项目(2021JJ60032)。

摘　　要：部分稳定氧化锆(Partially stabilized zirconia,PSZ)陶瓷因其优越的性能在航空航天工业等领域有广泛的应用。表面粗糙度是评价PSZ陶瓷磨削加工水平的关键指标,为了降低磨削表面粗糙度的预测误差,提出了一种基于相关性分析与卷积-双向长短期记忆神经网络(Convolution-bidirectional long short term memory neural network,CNN-BiLSTM)的PSZ陶瓷磨削表面粗糙度声发射预测模型。通过分析磨削声发射信号特征值与磨削表面粗糙度值之间相关性,筛选出磨削声发射信号与磨削表面粗糙度之间的最相关频段和特征矩阵,作为CNN-BiLSTM神经网络的输入参数以降低磨削表面粗糙度声发射预测的误差。研究结果表明,基于相关性分析与CNN-BiLSTM神经网络的PSZ陶瓷磨削表面粗糙度的平均预测误差低于3.92%。Partially stabilized zirconia(PSZ)is widely used in the aerospace industry and other fields due to its superior properties.Surface roughness is a key index to evaluate the grinding level of PSZ ceramics.In order to reduce the prediction error of grinding surface roughness,an acoustic emission(AE)prediction of grinding surface roughness of PSZ ceramics based on correlation analysis and convolution-bidirectional long short term memory(CNN-BiLSTM)neural network is proposed.The correlation between the eigenvalues and the grinding surface roughness values in different frequency bands in the grinding AE signals are analyzed,and the optimal sensitive frequency band and the feature matrix of the grinding AE signals are selected as the input parameters of the CNN-BiLSTM neural network to reduce the prediction error of acoustic emission of grinding surface roughness.The results show that the average prediction errors based on correlation analysis and CNN-BiLSTM neural network PSZ grinding surface roughness AE prediction is under 3.92%.

关 键 词：部分稳定氧化锆 磨削声发射 相关性分析 卷积-双向长短期记忆神经网络 表面粗糙度预测 

分 类 号：TG58[金属学及工艺—金属切削加工及机床]