Zirconia has been used in medical applications since last few years and an optimum and cost-effective condition in grinding zirconia has drawn industrial attention.This paper aimed to improve and control the surface i...Zirconia has been used in medical applications since last few years and an optimum and cost-effective condition in grinding zirconia has drawn industrial attention.This paper aimed to improve and control the surface integrity,flexural strength and grinding cost in grinding partially stabilized zirconia(PSZ)using a diamond grinding wheel.The phase transition and grindability of PSZ were also evaluated.Ground surfaces analysis shows that all samples subjected to the grinding presented an increase in surface integrity,and the subsurface damages 100 m below the surface were reduced from 3.4%to 0.9%.The flexural strength using 3 point bending test(3PB)shows that grinding increased the flexural strength more than 29%which is the result of higher surface integrity.The ground surfaces were analyzed using X-ray diffraction(XRD)and the results shows that T-M phase transition trend is in accordance with the surface integrity.In other words,XRD analyses prove that T-M phase transition results in higher flexural strength and surface integrity.It was also observed that in the best condition,the grinding cost was reduced by 72%.It can be concluded that controlling the grinding condition in grinding PSZ will result in the increase of the surface integrity and flexural strength.A mathematical model was created to find an optimum condition using response surface method(RSM).It is observed that feed rate has greater effect on the outputs rather than depth of cut.展开更多
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...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%。展开更多
文摘Zirconia has been used in medical applications since last few years and an optimum and cost-effective condition in grinding zirconia has drawn industrial attention.This paper aimed to improve and control the surface integrity,flexural strength and grinding cost in grinding partially stabilized zirconia(PSZ)using a diamond grinding wheel.The phase transition and grindability of PSZ were also evaluated.Ground surfaces analysis shows that all samples subjected to the grinding presented an increase in surface integrity,and the subsurface damages 100 m below the surface were reduced from 3.4%to 0.9%.The flexural strength using 3 point bending test(3PB)shows that grinding increased the flexural strength more than 29%which is the result of higher surface integrity.The ground surfaces were analyzed using X-ray diffraction(XRD)and the results shows that T-M phase transition trend is in accordance with the surface integrity.In other words,XRD analyses prove that T-M phase transition results in higher flexural strength and surface integrity.It was also observed that in the best condition,the grinding cost was reduced by 72%.It can be concluded that controlling the grinding condition in grinding PSZ will result in the increase of the surface integrity and flexural strength.A mathematical model was created to find an optimum condition using response surface method(RSM).It is observed that feed rate has greater effect on the outputs rather than depth of cut.
文摘部分稳定氧化锆(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%。
