铈掺杂对PZT压电陶瓷性能的影响

研究了铈掺杂对PZT(锆钛酸铅)压电陶瓷材料的相组成、微观结构、介电性能、压电性能及铁电性能的影响,并对实验结果做出物理机理的解释.实验结果表明,适量的铈掺杂有利于材料结构的致密,提高了体电阻率,解决了材料在高温高场下极化困难的问题,在铈掺杂量为0.4%(摩尔分数)时,制备出综合性能良好的PZT压电陶瓷:室温时εr=958,tgδ=0.24%,d33=239pC/N,Kp=0.45,Qm=886,适合制备大功率压电陶瓷.     

提高PZT压电性能方法的研究现状

PZT(锆钛酸铅)是应用最广泛的压电陶瓷。介绍了提高PZT压电陶瓷压电性能的方法,重点总结了采用改变锆钛比、进行掺杂改性和调节烧结温度等方法来改变压电性能的研究现状,进而为改进工艺提高压电性能提供理论支持,并展望了将PZT压电材料应用于智能涂层在线监测的前景,分析了亟待解决的问题。     

人工神经网络在优化BaTiO3陶瓷配方研究中的应用

首次将人工神经网络技术用于介电陶瓷的配方性能分析.以BaTiO3为研究对象选取了几种掺杂剂,在均匀实验设计的基础上,用BP人工神经网络对所得实验结果进行了分析,建立了相应配方的数学模型并将其与多重非线形回归模型的结果进行了比较.通过对人工神经网络配方数学模型的二次分析,得到了比多重非线形回归模型更加丰富的配方信息和内在规律,并且用图形化方式直观地表达了出来.在进一步对配方结果的优化和验证的基础上发现实验结果能够较好地符合理论预测,说明人工神经网络对于获得多性能指标要求介电陶瓷的最优化配方具有较好的指导作用.     

铟掺杂PZT铁电陶瓷性能研究

采用传统固相烧结法制备In2O3掺杂的锆钛酸铅(PZT)铁电陶瓷,研究了In2O3掺杂量对PZT铁电陶瓷材料的相组成、微观结构、介电性能、压电性能及铁电性能的影响。研究结果表明,随着In2O3掺杂量的增加,PZT材料在准同型相界处三方相增加四方相减少,适量掺入In2O3有利于晶粒均匀生长。在不同的铟掺杂剂量下,PZT陶瓷材料分别具有最佳的铁电及压电性能。当铟掺杂量为0.1%(质量分数)时,PZT材料具有最佳的铁电性能,其剩余极化强度为23.43μC/cm2,矫顽场为9.783kV/cm。当铟掺杂量为0.3%(质量分数)时,PZT材料具有最好的压电性能,其tanδ=0.023,d33=540pC/N,εr=1513,Kp=0.764,Qm=1819。     

PZT掺杂改性研究进展及其机理探究

PZT作为典型的钙钛矿结构,该结构为掺杂改性提供了较大的自由度。介绍了PZT压电材料掺杂改性的研究现状,简明扼要的分析了等价掺杂、高价掺杂、低价掺杂以及复合掺杂对压电材料各项性能的影响。并从掺杂离子对晶体结构和电畴结构影响两方面探讨了PZT掺杂改性的机理,进而为复合掺杂改性提供理论基础。     

人工神经网络在陶瓷材料中的应用

综述了人工神经网络在陶瓷材料中的应用研究进展,分析了人工神经网络在古陶瓷分类和传统陶瓷配方设计、优化及性能预测方面的应用,此外,对人工神经网络在功能陶瓷材料、陶瓷基复合材料中的应用进行了详述,并指出在应用中存在的问题及未来发展趋势.     

Sol-gel法制备PZT铁电薄膜的疲劳影响因素研究进展

简述了PZT铁电薄膜在制备和应用当中遇到的问题.围绕提高薄膜的疲劳性能,介绍了PZT铁电薄膜近年来在热处理、底电极、过渡层和掺杂等方面的研究进展,并分析了它们对材料电性能和疲劳性能的影响.最后提出了展望.     

基于压电理论的悬索桥工作状况智能监测与诊断方法

提出一种用压电材料（PZT）和人工神经网络控制理论相结合的方法,对悬索桥梁结构健康状态进行在线诊断,以便及时修复。该方法利用PZT受力变形时电压电场变化的机理采集信息,结合改进了的BP神经网络控制方法,对悬索桥梁的应力、应变进行在线分析和智能诊断。并通过实验研究获得大量的实验数据,对BP网络参数进行优化,证明了该方法的可行性和先进性。     

人工神经网络在优化BaTiO3陶瓷配方研究中的应用

首次将人工神经网络技术用于介电陶瓷的配方性能分析。以BaTiO3为研究对象选取了几种掺杂剂，在均匀实验设计的基础上，用BP人工神经网络对所得实验结果进行了分析，建立了相应配方的数学模型并将其与多重非线性回归模型的结果进行了比较。通过对人工神经网络配方数学模型的二次分析，得到了比多重非线形回归模型更加丰富的配方信息和内在规律，并且用图形化方式直观地表达了出来。在进一步对配方结果的优化和验证的基础上发现实验结果能够较好地符合理论预测，说明人工神经网络对于获得多性能指标要求介电陶瓷的最优化配方具有较好的指导作用。     

用凝胶注模成型制备压电陶瓷体及其电学性能研究

对PZT陶瓷浆料胶体化学特性进行了研究,成功制备了高固相含量低粘度的PZT陶瓷浆料.对含不同分散剂凝胶注模成型PZT样品电学性能的研究及其与普通干压法制备样品的比较表明,成型过程中的各种有机添加剂如单体和交联剂等不会对PZT的性能造成影响,而某些无机成份如选择不当的分散剂,则会起到一种掺杂剂的作用,从而影响成型后样品的各种电学性能.本文结果说明,对于电子陶瓷材料,在应用凝胶注模这种成型方法时,必须考虑各种添加剂可能对样品性能造成的影响     

Modeling constitutive relationship of Ti40 alloy using artificial neural network

Constitutive relationship equation reflects the highly non-linear relationship of flow stress as function of strain, strain rate and temperature. It is a necessary mathematical model that describes basic information of materials deformation and finite element simulation. In this paper, based on the experimental data obtained from Gleeble-1500 Thermal Simulator, the constitutive relationship model for Ti40 alloy has been developed using back propagation (BP) neural network. The predicted flow stress values were compared with the experimental values. It was found that the absolute relative error between predicted and experimental data is less than 8.0%, which shows that predicted flow stress by artificial neural network (ANN) model is in good agreement with experimental results. Moreover, the ANN model could describe the whole deforming process better, indicating that the present model can provide a convenient and effective way to establish the constitutive relationship for Ti40 alloy.     

Predicting the bake hardenability of steels using neural network modeling

In this work, an artificial neural network (ANN) model for prediction of mechanical properties of baked steels was established. The model introduced here considers the content of carbon, the prestrain amount, the initial yield stress and the baking temperature as inputs. While, the bake hardenability, work hardening values and yield stresses after steel baking are presented as outputs. The network was trained using the data from experimental work and back-propagation algorithm. The results show that the predicted values by the model are much more accurate than the experimental ones. The model suggested a two-stage strengthening for baking of ultra low carbon (ULC) steels, whereas, in the case of low carbon steels only one increment step in strength was reported. Comparing the predicted amounts by ANN model with the experimental ones indicates that well-trained neural network model provides very accurate results.     

Prediction of permeability coefficients of compounds through caco-2 cell monolayer using artificial neural network analysis

Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log k_p) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r² = 0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r² = 0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.     

Prediction of mechanical properties in magnesia based refractory materials using ANN

Refractory materials are heterogeneous materials having complex microstructures with different constituent’s properties. The mechanical properties of these materials change depending on their chemical composition and temperature. Therefore, it is important to select a refractory material, which is suitable for working conditions and is fit to place of use. Artificial neural network (ANN) model is established to investigate the relationship among processing parameters (chemical composition, temperature) and mechanical properties (bending strength, Young’s modulus) in magnesia based refractory materials. The mechanical properties of magnesia based refractory materials having four different chemical compositions were investigated using three point bending test at temperatures of 25, 400, 500, 600, 700, 800, 900, 1000 and 1400 °C.The bending strength (σ) and Young’s modulus (E) were theoretically calculated by ANN method and theoretical results were compared with experimental values for each temperature. There were insignificant differences between experimental values and ANN results meaning that ANN results can be used instead of experimental values. Thus, mechanical properties of refractory materials having different chemical composition can be predicted by using ANN method regardless of the treatment temperature.     

Predicting of mechanical properties of Fe–Mn–(Al, Si) TRIP/TWIP steels using neural network modeling

In this work, an artificial neural network (ANN) model was established in order to predict the mechanical properties of transformation induced plasticity/twinning induced plasticity (TRIP/TWIP) steels. The model developed in this study was consider the contents of Mn (15–30 wt%), Si (2–4 wt%) and Al (2–4 wt%) as inputs, while, the total elongation, yield strength and tensile strength are presented as outputs. The optimal ANN architecture and training algorithm were determined. Comparing the predicted values by ANN with the experimental data indicates that trained neural network model provides accurate results.     

Prediction of Permeability Coefficients of Compounds Through Caco-2 Cell Monolayer Using Artificial Neural Network Analysis

Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log k_p) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r² = 0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r² = 0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.     

Phase Identification of La-Doped Hard Magnetic Barium Ferrite Using Artificial Neural Network

A model based on an artificial neural network (ANN) was designed for the simulation and estimation of 2 theta and intensity values obtained by X-Ray Diffraction (XRD) of pure and La-doped barium ferrite powders which have been synthesized in ammonium nitrate melt. Its performance is evaluated by the influences of different La content, sintering temperature, Fe/Ba ratio, and washed in HCl (or not washed in HCl) samples. The XRD patterns of samples estimated by the ANN agree well with the experimental values, indicating that the model is reliable and adequate.     

Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool

In this study, the influence of hardness (H) and spindle speed (N) on surface roughness (Ra) in hard turning operation of AISI 4140 using CBN cutting tool has been studied. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental values and to show the effect of hardness and spindle speed on the surface roughness. Artificial neural network (ANN) and regression methods have been used for modelling of surface roughness in hard turning operation of AISI 4140 using CBN cutting tool. The input parameters are selected to be as hardness and spindle speed and the output is the surface roughness. Regression and artificial neural network optimum models have been presented for predicting surface roughness. The predicted surface roughness by the employed models has been compared with the experimental data which shows the preference of ANN in prediction of surface roughness during hard turning operation. Finally, a reverse ANN model is constructed to estimate the hardness and spindle speed from surface roughness values. The results indicate that the reverse ANN model can predict hardness for the train data and spindle speed for the test data with a good accuracy but the predicted spindle speed for the train data and the predicted hardness for the test data don’t have acceptable accuracy.     

Artificial neural network modelling to predict hot deformation behaviour of as HIPed FGH4169 superalloy

Abstract

The hot deformation behaviour of as HIPed FGH4169 superalloy was studied by single stroke compression test on MMS-200 test machine at the temperatures of 950–1050°C and the strain rates of 0·004–10 s^?1. Based on the experimental results, a back-propagation artificial neural network model and constitutive equation method were established to predict the flow stress of FGH4169 superalloy. The predictability of two different models was compared. The correlation coefficients of experimental and predicted flow stress with the trained BP ANN model and constitutive equation were 0·9995 and 0·9808 respectively. The average root mean square error (RMSE) values of the trained ANN model and constitutive equation are 0·39 and 2·21 MPa respectively. And the average absolute relative error (AARE) values of the trained ANN model and constitutive equation are 1·79 and 7·47% respectively. The results showed that the ANN model is an effective tool to predict the flow stress in comparison with constitutive equation.     

Developing a model for hardness prediction in water-quenched and tempered AISI 1045 steel through an artificial neural network

The aim of the current study was to develop an artificial neural network (ANN) model to predict the hardness drop of the water-quenched and tempered AISI 1045 steel specimens, as a function of tempering temperature and time parameters. In the first stage, the effects of selected tempering parameters on the hardness drop value were investigated. In the second stage, a group of data, which have been obtained from experiments, was used for training of the ANN model. Likewise, another group of experimental data was utilized for the ANN model validation. Ultimately, maximum error of the ANN prediction was determined. The agreement between the predicted values of the ANN model with the experimental data was found to be reasonably good.