A Hybrid Compensation Scheme for the Input Rate-Dependent Hysteresis of the Piezoelectric Ceramic Actuators

A hybrid compensation scheme for piezoelectric ceramic actuators(PEAs)is proposed.In the hybrid compensation scheme,the input rate-dependent hysteresis characteristics of the PEAs are compensated.The feedforward controller is a novel input rate-dependent neural network hysteresis inverse model,while the feedback controller is a proportion integration differentiation(PID)controller.In the proposed inverse model,an input ratedependent auxiliary inverse operator(RAIO)and output of the hysteresis construct the expanded input space(EIS)of the inverse model which transforms the hysteresis inverse with multi-valued mapping into single-valued mapping,and the wiping-out,rate-dependent and continuous properties of the RAIO are analyzed in theories.Based on the EIS method,a hysteresis neural network inverse model,namely the dynamic back propagation neural network(DBPNN)model,is established.Moreover,a hybrid compensation scheme for the PEAs is designed to compensate for the hysteresis.Finally,the proposed method,the conventional PID controller and the hybrid controller with the modified input rate-dependent Prandtl-Ishlinskii(MRPI)model are all applied in the experimental platform.Experimental results show that the proposed method has obvious superiorities in the performance of the system.

Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times

A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.

A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

Ceramic tiles are one of the most indispensable materials for interior decoration.The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures.In this paper,we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network(GAN).The generated tile images can be tailored to meet the specific needs of the user for the tile textures.The proposed method consists of four steps.Firstly,a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN.Secondly,for each ceramic tile image in the dataset,the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated sketches.Thirdly,the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training speed.Finally,we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of“one style with multiple faces”.We conduct the training process of the proposed generation network on 2583 ceramic tile images dataset.To measure the generative diversity and quality,we use Frechet Inception Distance(FID)and Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)metrics.The experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images,with FID of 32.47 and BRISQUE of 28.44.

Effects of ZnO,FeO and Fe_(2)O_(3)on the spinel formation,microstructure and physicochemical properties of augite-based glass ceramics

Augite-based glass ceramics were synthesised using ZnO,FeO,and Fe_(2)O_(3)as additives,and the spinel formation,matrix structure,crystallisation thermodynamics,and physicochemical properties were investigated.The results showed that oxides resulted in numerous preliminary spinels in the glass matrix.FeO,ZnO,and Fe_(2)O_(3)influenced the formation of spinel,while FeO simplified the glass network.FeO and ZnO promoted bulk crystallisation of the parent glass.After adding oxides,the grains of augite phase were refined,and the relative quantities of augite crystal planes were also influenced.All samples displayed good mechanical properties and chemical stability.The 2wt%ZnO-doping sample displayed the maximum flexural strength(170.3 MPa).Chromium leaching amount values of all the samples were less than the national standard(1.5 mg/L),confirming the safety of the materials.In conclusion,an appropriate amount of zinc-containing raw material is beneficial for the preparation of augite-based glass ceramics.

Predicting model on ultimate compressive strength of Al_2O_3-ZrO_2 ceramic foam filter based on BP neural network

In present study, BP neural network model was proposed for the prediction of ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The inputs of the BP neural network model were the applied load on the epispastic polystyrene template （F）, centrifugal acceleration （v） and sintering temperature （T）, while the only output was the ultimate compressive strength （（7）. According to the registered BP model, the effects of F, v, T on 0 were analyzed. The predicted results agree with the actual data within reasonable experimental error, indicating that the BP model is practically a very useful tool in property prediction and process parameter design of the Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting.

Processing and mechanical properties of network ceramic/steel composites by pressureless infiltration

A new style Ni-containing alumina ceramic foam based continuous three-dimensional interconnected skeleton was prepared by impregnating a polymeric sponge with aqueous ceramic slurry.Subsequently,alumina ceramic foam/steel metal matrix composites(MMCs) were prepared successfully by sand mold casting technique.The microstructure and mechanical properties of MMCs were investigated by SEM,EDS and compressive test.The results show that the depth of infiltration is about 40 μm to the bonding interface of ceramic/steel and the fracture strength σmax and plastic strain limit εp of composite are 520 MPa and 11.2%,respectively.The fretting wear mechanism of MMCs is mainly performed at the oxidative wear mode with lower load/friction frequency and the predominant oxidation wear together with slight adhesive wear and abrasive wear multiple mode with higher load/ friction frequency.Moreover,the infiltration bonding and continuous three-dimensional interconnected ceramic skeleton play a vital role in the stability of the bonding interface and excellent mechanical properties.

Modeling and analysis of porosity and compressive strength of gradient Al_2O_3-ZrO_2 ceramic lter using BP neural network

BP neural network was used in this study to model the porosity and the compressive strength of a gradient Al2Q-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The influences of the load applied on the epispastic polystyrene template （F）, the centrifugal acceleration （V） and sintering temperature （T） on the porosity （P） and compressive strength （a） of the sintered products were studied by using the registered three-layer BP model. The accuracy of the model was verified by comparing the BP model predicted results with the experimental ones. Results show that the model prediction agrees with the experimental data within a reasonable experimental error, indicating that the three-layer BP network based modeling is effective in predicting both the properties and processing parameters in designing the gradient Al203-ZrO2 ceramic foam filter. The prediction results show that the porosity percentage increases and compressive strength decreases with an increase in the applied load on epispastic polystyrene template. As for the influence of sintering temperature, the porosity percentage decreases monotonically with an increase in sintering temperature, yet the compressive strength first increases and then decreases slightly in a given temperature range. Furthermore, the porosity percentage changes little but the compressive strength first increases and then decreases when the centrifugal acceleration increases.

The Conformity Utilization of Ceramic Culture Native Digital Resources Based On the Concept of WEB3.0

In Chinese traditional culture, ceramic culture is a very special cultural system, With the advent of the era of network and the particularity of ceramic culture, Make the network native digital resources development and utilization is especially important. The idea of Web3.0 make important ceramic culture network native digital resources ＂reasonable configuration＂, t o be more embody the value of academic research. In this paper, we＇ll discuss the conformity utilization of ceramic culture native digital resources based on the concept of WEB3.0.

The Conformity Utilization of Ceramic Culture Native Digital Resources Based On the Concept of WEB3.0

In Chinese traditional culture, ceramic culture is a very special cultural system With the advent of the era of network and the particularity of ceramic culture, Make the network native digital resources development and utilization is especially important. The idea of Web3.0 make important ceramic culture network native digital resources ＂reasonable configuration＂, t o be more embody the value of academic research. In this paper, we＇ll discuss the conformity utilization of ceramic culture native digital resources based on the concept of WEB3.0.

铋酸盐封接玻璃的研究及其连接应用现状

无铅低温封接玻璃由于具有绿色安全、成本低、封接温度低等优势,被广泛应用于航空航天、电真空、集成电路等领域。在磷酸盐、钒酸盐、硼酸盐和铋酸盐等多种封接玻璃体系中,因铋与铅具有相似的性质特点,使得铋酸盐玻璃成为最有可能替代铅酸盐玻璃的绿色玻璃钎料。综述了铋酸盐玻璃的成分和性能调控、网络结构的研究现状,介绍了目前使用铋酸盐封接玻璃进行金属、陶瓷同质/异质材料连接的研究进展,最后对未来的发展趋势进行了展望。

网状孔壁碳化硅基多孔陶瓷的制备及其颗粒物过滤研究

为了提高碳化硅基多孔陶瓷的过滤性能,以碳化硅粉体、氧化铝粉体、聚醚多元醇、多亚甲基多苯基异氰酸酯、二月桂酸二丁基锡、三乙烯二胺、稳泡剂硅油(阿拉丁)为原料,通过聚氨酯发泡技术,研制了三维(3D)网状孔壁结构的碳化硅基多孔陶瓷试样,并探索了料浆固含量(固相质量分数分别为45%、50%、55%和60%)对试样显微结构、物理性能和颗粒物过滤性能的影响。结果表明:1)随着固含量的升高,试样体积密度和常温耐压强度提高,显气孔率降低;2)聚氨酯发泡过程中产生的微孔会分布在陶瓷孔壁上形成网状孔壁结构,增大颗粒物与孔壁碰撞概率,提高试样的过滤效率;同时,网状孔壁结构还可以提高试样气孔贯通性,降低其压降;3)当固含量为55%(w)时,试样体积密度约为0.57 g·cm^(-3),显气孔率为79.2%,常温耐压强度为3.7 MPa;且对颗粒物的去除率高达91.2%,具有良好的再生性能。

医用氧化锆陶瓷磨削表面粗糙度的声发射智能预测

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。

基于EMI-CNN的建筑施工模板支撑体系节点健康监测

为预防模板坍塌引发建筑施工安全事故风险,提出一种基于压电阻抗法(EMI)和卷积神经网络(CNN)的模板支撑体系节点智能监测方法。首先,利用压电陶瓷传感器(PZT)的机电耦合特性及其集驱动-传感于一体的特点,建立PZT-节点耦合系统的机电阻抗传感机制模型;其次,基于EMI法,以与待测结构耦合的PZT片电导信号为监测指标,确定模板支撑体系节点松动的发生;然后,以敏感频段内PZT片的801个原始电导信号为模型输入,9个节点松动程度为模型输出,构建162组学习样本和27组测试样本,建立EMI-CNN模型,确定节点松动程度;最后,以一个实际工程中的建筑施工模板体系节点为例,验证EMI-CNN模型的有效性,并对比分析EMI-BP模型。研究结果表明:EMI-CNN模型经过85次迭代达到收敛,预测准确率达到100%,相较于EMI-BP模型提高29.63%。该监测方法可实现对建筑施工模板支撑体系节点健康状态实时、准确、无损监测。

可切削聚合物渗透陶瓷在口腔修复中应用的研究进展

聚合物渗透陶瓷(PICN)作为一种新兴的口腔修复材料,因其独特的陶瓷-树脂双相互穿网络结构而受到广泛关注。该材料融合了陶瓷和树脂的优秀特性,能有效抵抗裂纹扩展,保护基牙及对颌牙,且具备出色的粘接性,在单冠、嵌体、部分冠和贴面修复方面展现出了良好的应用潜力和临床效果。本综述围绕PICN材料的组成、结构、力学性能、粘接性能、耐磨性、光学特性以及临床表现等方面进行详细阐述,并对其未来的发展趋势和应用前景进行展望。

基于熔池热历史的陶瓷增强金属基复材激光定向能量沉积质量实时监测方法

针对激光定向能量沉积(L-DED)制备陶瓷增强金属基复合材料(CRMMC)过程中成形质量不稳定的问题,提出一种基于熔池热历史的CRMMC质量监测方法。为实现在CPU硬件上的实时监测,构建了单路结构的轻量级全深度可分离卷积神经网络模型(FD-Net)。输入9个不同激光能量制备不同状态的CRMMC成形质量,使用红外热像仪同步采集熔池红外图像作为数据集训练和测试FD-Net,并与当前先进的轻量级卷积神经网络(CNN)模型进行性能对比。结果表明:FD-Net在Inter-CPU上以7.90ms/帧的推理时间实现了高精度监测,显著低于其他CNN模型,证明所提方法可在工业微型计算机上实现CRMMC质量状态的实时监测。

基于神经网络的蜂窝陶瓷蓄热体温度效率预测

针对蜂窝陶瓷蓄热体在通风瓦斯蓄热氧化过程中温度效率难以直接预测的问题,提出一种基于神经网络的蜂窝陶瓷蓄热体温度效率预测方法。首先选择蜂窝陶瓷蓄热体单孔截面形状、截面积、壁厚、高度和高温烟气入口速度作为研究对象,其次采用计算流体动力软件模拟仿真通风瓦斯蓄热氧化过程,最后将仿真计算所得的230组蜂窝陶瓷蓄热体温度效率数据作为学习样本,30组数据作为验证样本,同时研究隐含层节点数与BP神经网络预测精度之间的关系。结果表明,当隐含节点数为12时,神经网络的最优平均预测误差为0.63%,可以用于快速准确预测通风瓦斯蓄热氧化过程中蜂窝陶瓷蓄热体的温度效率。

基于FPGA+STM32的多功能压电陶瓷控制系统

针对微位移压电陶瓷控制系统数据传输方式单一、通道少、可靠性低等缺点,设计一种FPGA+STM32的多功能压电陶瓷控制系统。该系统以光纤为主通信方案,网络+FSMC总线为备通信方案;并以Artix7系列FPGA芯片为主控芯片,负责接收4路光纤与FSMC总线传输的数据和命令,并行控制19片16通道的DAC81416数/模转换,实现304通道的模拟电压输出,而STM32负责网络与FSMC总线的数据传输控制;同时,采用桥式电路+差分放大电路+模/数转换器的电路设计与温湿度传感器相结合的方式采集环境数据,并通过RS 422接口实现25 Hz频率回传。此外,预留RS 485接口,可控制外部环境测量装置,完成更多环境数据的采集。目前,该系统已成功应用于某光学系统的压电陶瓷位移控制中。测试结果表明,整个系统工作稳定,两种通信方式均可实现高速数据传输,通过配置可实现304通道-5~5 V的模拟电压实时输出,环境数据能按照25 Hz频率实时刷新显示。

一种基于Clos网络的新型4×4全交换开关矩阵

文中首次将Clos网络结构应用于4×4全交换开关矩阵中,采用多芯片模组技术将低温共烧陶瓷(LTCC)和双刀双掷芯片进行集成化设计。新设计克服了传统单刀四掷方案接口数量多、布线复杂的缺点,以更为简捷的拓扑形式实现了全交换功能。该设计结合LTCC多层堆叠工艺和具体芯片接口,给出了切实可行的叠层结构和互连方案,并以此研制出一款驻波优良、通道一致性好、隔离度高的C频段4×4开关矩阵。文中设计实现方法简单、可靠,适用于其他形式或更高频率的开关矩阵设计。

基于网络文本分析的文化旅游体验研究——以景德镇古窑民俗博览区为例

以景德镇古窑民俗博览区为例,借助ROST Content Mining 6工具,使用文本内容分析法探究游客在文化类旅游景区旅游过程中的旅游体验感知特征。研究结果表明:游客对景德镇古窑民俗博览区的正面感知大于负面感知,满意度较高,但仍有较大的提升空间;游客的旅游动机主要为教育和当地特色文化;游客行为中,互动关系与参观时间与旅游体验好坏密切相关;游客对景区核心吸引力感知明显,但对于初次接触陶瓷文化的游客来说,存在“看不懂”“听不懂”障碍。影响游客旅游体验的主要问题集中在票价、设施、管理与服务等方面。并由此提出旅游体验提升的三个方面:注重游客文化体验感、提升游客旅游信任感和提高游客文化获得感。

轴承表面 Al_(2)O_(3) 基陶瓷绝缘涂层的粗糙度预测

为了提升轴承表面Al_(2)O_(3)基陶瓷绝缘涂层的粗糙度预测精度,提出基于光谱共焦原理的砂轮表面测量及磨粒特征参数量化方法,以砂轮表面的磨粒特征参数K,砂轮线速度vs,工件进给速度f,切削深度ap及法向磨削力F为输入参数,建立能够直接反映砂轮表面时变状态的工件表面粗糙度BP神经网络预测模型,并通过已知磨削样本及砂轮磨损后的4组未知样本对网络预测模型性能进行验证。结果表明:已知样本的BP网络模型粗糙度预测结果与实际结果的规律及数值较为一致,其网络输出误差均<±0.04μm;4组未知样本的网络预测精度下降,但其相对误差最大值的绝对值不超过20.00%。建立的包含砂轮表面磨粒特征参数的神经网络预测模型,可以适应砂轮磨粒磨损时变状态下的轴承表面Al_(2)O_(3)基陶瓷绝缘涂层的粗糙度预测,且其对未知样本具有一定的泛化能力。