【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%。更多还原

【Abstract】 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%.更多还原