矿渣微晶玻璃专家系统经验设计

分析了矿渣微晶玻璃材料设计知识的特点,确定了合适的知识表示方法.模拟了材料设计专家进行材料设计的思路,建立了矿渣微晶玻璃材料设计专家系统的经验设计模块,并由实例验证其具有很好的设计效果.     

矿渣微晶玻璃研究与进展

回顾了矿渣微晶玻璃的研究历史，着重分析了不同主晶相的矿渣微晶玻璃的组成，结构与性能，对矿渣微晶玻璃的发展趋势和待解决的问题作了探讨。最后评述了我国在矿渣微晶玻璃开发与应用上与国外的差距。     

工业及生活废渣制备彩色微晶玻璃的研究

介绍了国内外以冶金矿渣、尾矿渣及粉煤灰、城市垃圾焚烧飞灰为主要原料的废渣微晶玻璃的研究概况,分别对其组成、结构与性能、种类与制备等方面做了分析。重点综述了彩色废渣微晶玻璃的研究现状,在废渣微晶玻璃的基础上,调节玻璃组分,以硒粉、氧化铬、氧化锰等作为着色剂,通过采用一次着色或二次着色工艺,可制备出色彩丰富的废渣微晶玻璃,市场潜力巨大。最后展望了工业及生活废渣制备微晶玻璃的未来发展。     

高炉矿渣二次资源合成绿色无机材料的研究进展

以高炉矿渣为原料合成绿色无机材料,不仅为固体废弃物的深度利用提供了一条新的思路,而且为多种无机材料的廉价合成提供了一条新的绿色工艺.在简单介绍高炉矿渣矿物及化学组成的基础上,综述了利用高炉矿渣二次资源合成Sialon、水泥及混凝土、涂料、胶凝材料、矿渣纤维、硅灰石、多孔陶粒、泡沫材料、微晶玻璃、透水砖等绿色无机材料的研究进展,并对其发展前景进行了展望.     

工业废物微晶玻璃组分设计和制备工艺的研究进展

工业废物微晶玻璃是一种以工业废物为主要原料制备的含有微晶相的绿色材料。在讨论不同化学成分对基础玻璃熔融和晶化行为影响的基础上,总结了工业废物微晶玻璃的配方设计;综述了不同制备工艺对工业废物微晶玻璃结构与性能的影响,最后展望了工业废物微晶玻璃的应用前景。     

β-锂霞石负膨胀微晶玻璃的制备技术及结构特征

研究了β锂霞石微晶玻璃的制备技术、结构特征及其负膨胀特征。首先采用玻璃结晶法制备β锂霞石负膨胀微晶玻璃材料,然后通过XRD、SEM等测试手段,表征了β锂霞石微晶玻璃材料的结构特征。并讨论β锂霞石负膨胀微晶玻璃的膨胀系数及其与晶相组成和晶化温度及时间的依从关系,使其负膨胀系数在一定范围内连续可调。研究并制备出热膨胀系数可达到为-1.037×10-5/℃的β锂霞石微晶玻璃。     

磁记忆存储器微晶玻璃基板

介绍了适用于高容量，小型化磁记录存储器基板的各种微晶玻璃材料，详细讨论了微晶玻璃的化学组成，显微结构，主晶相种类以及微晶玻璃的物化性能和表面特性，并与ＮｉＰ／Ａｌ基板材料性能进行了对比，简要介绍了硬盘微晶玻璃基板的实用性研究结果。     

功能微晶玻璃的研究现状及发展趋势

功能微晶玻璃是由非晶态无机材料经受控晶化而获得的一类具有均匀致密微晶结构的新型功能材料。综述了具有良好的光学，电学，磁学和生物等功能的微晶玻璃的研究现状，探讨了功能微晶玻璃的发展趋势及值得关注的几个问题。     

Nb2O5对透辉石基矿渣微晶玻璃显微结构和力学性能的影响

为研究Nb_2O_5对透辉石基矿渣微晶玻璃显微结构和力学性能的影响机理,以富铁白云鄂博西尾矿、粉煤灰为主要原料,采用熔融工艺制备了添加质量分数0%~4%Nb_2O_5的透辉石基矿渣微晶玻璃。DTA、XRD、SEM和力学测试结果表明,Nb_2O_5主要以Ca2Nb2O7第二相的形式存在于辉石相界,其含量随Nb_2O_5添加量升高而增大。同时辉石主晶相从类菊花状枝晶组织转变成平均尺寸逐渐减小的圆角岛状组织。微晶玻璃的抗折强度平均为207 MPa,当Nb_2O_5质量分数为2%时最高,达236 MPa。     

化学组成对微晶玻璃/金属复合材料性能的影响

研究了一种新型装饰材料—微晶玻璃/金属复合材料。在微晶玻璃与金属基体复合过程中,基础玻璃组分的选择对两者结合质量有较大的影响,借助XRD、DTA及各种性能测试手段对基础玻璃及微晶玻璃和复合材料试样进行了研究,结果表明,ZnO/Al2O3>1组成的微晶玻璃与金属达到了良好的匹配,具有较好的结合强度。     

Radial basis function neural network for pulse radar detection

A new approach using a radial basis function network (RBFN) for pulse compression is proposed. In the study, networks using 13-element Barker code, 35-element Barker code and 21-bit optimal sequences have been implemented. In training these networks, the RBFN-based learning algorithm was used. Simulation results show that RBFN approach has significant improvement in error convergence speed (very low training error), superior signal-to-sidelobe ratios, good noise rejection performance, improved misalignment performance, good range resolution ability and improved Doppler shift performance compared to other neural network approaches such as back-propagation, extended Kalman filter and autocorrelation function based learning algorithms. The proposed neural network approach provides a robust mean for pulse radar tracking     

中钛型含钛高炉渣制微晶玻璃及其性能研究

以中钛型含钛高炉渣为主原料制备微晶玻璃,利用渣中的TiO2作晶核剂.采用差示扫描量热法(DSC)、X射线衍射(XRD)和扫描电子显微镜(SEM)等分析技术研究了含钛高炉渣用量的变化对基础玻璃晶化、微晶玻璃性能的影响.结果表明,渣中适量的TiO2对玻璃晶化有较好的促进作用.渣用量较低时制得的微晶玻璃的主晶相为硅灰石,但当渣用量超过70％时,主晶相发生变化,变为钙铝黄长石等长石类矿相.中钛型含钛高炉渣用量为63％左右时,制得的微晶玻璃晶相含量合适,性能最好.此时采用的热处理制度为:核化温度720℃,保温1h,晶化温度945℃,保温2h,制得的微晶玻璃抗弯强度为121.68MPa,显微硬度为7.81 GPa.     

枕式包装机包装材料供送速度控制

目的 为了提高包装袋的袋长精度,提升包装袋体外观质量。方法 提出一种基于神经网络PID自适应的三伺服枕式包装机包装材料速度控制方法,将传统的PID控制方法同神经网络控制相结合,设计一个神经网络PID控制器,包括控制器结构和学习算法,可用于解决相关非线性问题。结果 仿真和实验结果表明,采用神经网络PID控制方法,包装材料速度达到稳态时,所用时间约为2 s,最大超调量不超过2%,包装袋长误差能够有效控制在±1 mm以内。结论 所设计的控制方法与传统的PID控制相比,具有响应速度快、抗干扰能力强、控制输出稳定等优点,能够显著提高包装袋长精度。     

A neural network-based algorithm that searches for the measuring points of a rule surface

The purpose of this study is to use a proposed neural network-based algorithm to explore the determination of the recommended measuring points for a rule surface. The task of measuring a rule surface starts from the rule surface design blueprint. Mesh grid data on the designed rule surface were selected. The pattern recognition capability of the back-propagation neural network is explored in this article. The network learning was successfully performed by the learning and testing of the network, the support of a designated acceptable perpendicular error value, a learning model in which training examples were gradually added and the adjustment of the number of training examples according to the network structure.     

基于模糊学习算法的空调机组性能模拟

基于模糊熵准则和误差平方和准则建立了模糊学习算法,基于该模糊学习算法,应用BP神经网络对柜式空调机组的性能进行了模拟.结果表明,与传统的基于误差平方和准则的学习算法相比,采用模糊学习算法可以大大简化网络结构,有效提高模拟精度和效率.     

玻纖復合材料制成矽灰石之研究

臺灣海洋大學材料工程研究所過去6年来至今,針對可燃與不可燃醫療廢弃物、石化産業有害廢弃物、模擬放射性廢弃物與可燃與各類不可燃廢弃物作一系列高温電漿熔融處理研究.此次研究處理對象爲不可燃廢弃物中的Fiber Reinforced Plastic(FRP)復合材料、刺綱.研究配比中添加了玻璃以改變化學成份,經高温電漿熔融后熔岩具有高硬度、硬度平均值,低濾出率與非晶結構.添加特殊成分調節劑於熔岩粉末中均匀混合與特殊熱處理后,産生乳白色矽灰石玻璃陶瓷材料.     

卷积神经网络在印刷品缺陷检测的应用

目的 为了改善传统机器检测印刷产品缺陷存在误费率高的不足。方法 提出以卷积神经网络为控制核心的印刷品缺陷检测系统。设计可在实际检测中应用的卷积神经网络,设计在线印刷质量检测系统的硬件结构。结果 对结构相同而训练次数、学习率不同的卷积神经网络进行了缺陷检测的性能对比,验证了该卷积神经网络在学习率小于0.01时,可以获得较好的识别效果;在学习率大于0.05时,网络不容易收敛。网络训练次数越多,精度越高,相应的训练时间也较长。在满足快速性和精确度的条件下,确定了适应某印刷品的缺陷检验网络训练次数为50,学习率为0.005,此时的识别率为90%。结论 经过实验证明,该检测系统具有良好的缺陷识别能力,缺陷类型的分类准确率较高。该系统具有一定的实用价值。     

Recurrent RBFN-based fuzzy neural network control for X-Y-theta motion control stage using linear ultrasonic motors

A recurrent radial basis function network (RBFN) based fuzzy neural network (FNN) control system is proposed to control the position of an X-Y-theta motion control stage using linear ultrasonic motors (LUSMs) to track various contours in this study. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, the structure and the parameter learning phases of the recurrent RBFN-based FNN are performed concurrently and on line. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient decent method using a delta adaptation law. The experimental results due to various contours show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties.     

Prediction of die casting process parameters by using an artificial neural network model for zinc alloys

Pressure die casting is an important production process. In pressure die casting, the first setting of process parameters is established through guess work. Experts use their previous experience and knowledge to develop a solution for a new application. Due to rapid expansion in the die casting process to produce better quality products in a short period of time, there is ever increasing demand to replace the time-consuming and expert-reliant traditional trial and error methods of establishing process parameters. A neural network system is developed to generate the process parameters for the pressure die casting process. The system aims to replace the existing high-cost, time-consuming and expertdependent trial and error approach for determining the process parameters. The scope of this work includes analysing a physical model of the pressure die casting filling stage based on governing equations of die cavity filling and the collection of feasible casting data for the training of the network. The training data were generated by using ZN-DA3 material on a hot chamber die casting machine with a plunger diameter of 60 mm. The present network was developed using the MATLAB application toolbox. In this work, the neural network was developed by comparing three different training algorithms: i.e. error backpropagation algorithm; momentum and adaptive learning algorithm; and Levenberg-Marquardt approximation algorithm. It was found that the Levenberg-Marquardt approximation algorithm was the preferred method for this application as it reduced the sum-squared error to a small value. The accuracy of the developed network was tested by comparing the data generated from the network with those of an expert from a local die casting industry. It was established that by using this network the selection of process parameters becomes much easier, so that it can be used by a novice user without prior knowledge of the die casting process or optimization techniques.     

皮带秤动态称重用神经网络算法设计

针对皮带秤在使用中难以保持标称计量精度的缺点,提出将过程神经网络引入皮带秤动态称重误差的补偿中。将动态称量过程中皮带秤单位长度上的重量、皮带速度、皮带垂度变化作为模型输入,设计了应用于皮带秤动态称重误差研究的单隐层过程神经网络误差反传播学习算法,利用Matlab软件对算法模型进行训练和测试,模型经过149次学习优化达到网络精度要求,测试组误差为1%,较使用网络前的原误差明显降低,验证了算法的可行性和有效性。