稳健估计用于氮化铝金属化工艺的研究

应用化学镀镍的方法实现了氮化铝的金属化。为得到较大的氮化铝金属化层粘附力，运用基于稳健估计的神经网络研究氮化铝金属化中化学镀镍的反应参数与金属层粘附力的关系。为使神经网络更加稳健，本文根据统计学原理，在前馈神经网络基础上，采取稳健估计方法改进神经网络。建立了定量预测粘附力性能的模型，并进行实验验证。确定金属化工艺中稳定的优化工作区域。结果表明，稳健估计方法既有传统神经网络的优点，又有较强的抵抗异常     

稳健估计用于氮化铝金属化工艺的研究

应用化学镀镍的方法实现了氮化铝的金属化。为得到较大的氮化铝金属化层粘附力,运用基于稳健估计的神经网络研究氮化铝金属化学镀镍的反应参数与金属层粘附力的关系。为使神经网络更加稳健,本文根据统计学原理,在前馈神经网络基础上,采取稳健估计方法改进神经网络。建立了定量预测粘附力性能的模型,并进行实验验证。确定金属化工艺中稳定的优化工作区域。结果表明,稳健估计方法既有传统神经网络的优点,又有较强的抵抗异常值的能力,具有较广泛的实用性。     

石墨粉表面滚镀镍工艺优化

为了探究石墨粉表面金属化处理的新工艺,采用滚镀方法对40～60目的石墨粉滚镀镍层,运用正交试验考察了氯化铵浓度、镀液pH值和滚镀瓶转速等3因素对石墨粉表面镀镍含量的影响。用镀镍石墨粉在浓硝酸中充分腐蚀前后的质量差与腐蚀前镀镍石墨粉质量的比值表征试验工艺的镀镍含量;用扫描电镜观察了镍层的微观结构,优选出了镀镍效率高且稳定的工艺参数。结果表明:5 g/L氯化铵,pH值为3,滚镀瓶转速为10 r/min条件下滚镀镍层的镍含量较高,镀层形貌均匀,石墨粉颗粒间无粘结现象,镀层与石墨粉表面结合力良好。     

利用无电镀金实现微结构金属化的技术

无电镀是金属化的一种重要手段,具有成本低、保形性好、简单易操作等特点．在以前报道的MEMS结构无电镀金属化过程中,金属和硅结构会全部被金属化,难以形成复杂结构的局部选择性金属化．为此,采用一种商用无氰配方无电镀金溶液,通过两步无电镀方法,控制实验样品的激活时间、水浴温度等条件和两步镀覆的时间,使用无电镀镍作为中间层,实现了在特定材料层上的无电镀金;其针孔密度低,表面平整度较好,具有高的选择性,为高性能器件的研制打下了基础．     

新型电子陶瓷材料氮化铝工艺进展与应用前景

该简要回顾了新型电子陶瓷材料氮化铝的发展历程，探讨了其制造工艺与金属化工艺，分析了其在技术性能方面的优缺点，并研究了最新应用领域。     

化学镀时间对无纺布表面镍镀层结构影响规律研究

采用Pd2+活化结合化学镀镍工艺制备金属化的植物纤维无纺布,借助X射线衍射、扫描电镜、四探针等分析测试手段,研究化学镀时间对植物纤维无纺布表面镀镍层结构特性的影响规律。结果表明延长化学镀时间,植物纤维无纺布表面化学镀镍层结晶性能变好。当化学镀时间为10min时,植物纤维无纺布的质量为0.038g/cm2,镀层表面电阻率为0.2Ω·cm。     

芳纶纤维表面化学镀镍的研究

采用化学镀技术,实现了芳纶纤维表面镀镍金属化,利用SEM、EDS和XRD分别对芳纶纤维原始样品、粗化后及施镀后的表面形貌、原始纤维及镀层的成分和物相组成进行了分析比较,并用冷热循环法对镀镍芳纶纤维进行了结合力测试。结果表明:芳纶纤维化学镀镍后呈灰黑色,表面基本均匀光滑。镀层与基体结合力良好,镀层厚度可达0.375μm左右,镀层中镍含量在89.58%以上,且以单质镍为主,镀层晶粒细小,基本为纳米晶。     

碳纤维连续镀镍生产工艺及其屏蔽复合材料

为了提高碳纤维镀镍工艺的生产效率以及对填充复合型电磁屏蔽材料的开发,使用自行研发的碳纤维(CF)连续电镀镍生产设备生产镀镍碳纤维(Ni-CF),并制备了镀镍碳纤维增强丙烯腈-丁二烯-苯乙烯共聚物(ABS)复合材料(Ni-CF/ABS),研究了偶联剂对复合材料力学性能的影响,以及纤维金属化及纤维含量对复合材料电磁屏蔽性能的影响。结果表明,偶联剂使复合材料具有更好的力学性能,拉伸和弯曲强度分别达到41 MPa和61.4 MPa。纤维质量分数为12%时,复合材料达到最佳的电磁屏蔽效能。     

压电复合材料表面化学镀镍工艺及镀层性能

采用化学镀镍的方法对压电复合材料进行金属化。通过正交试验并结合实际生产, 研究确定了压电复合材料表面化学镀镍前处理中新型粗化液、活化液以及化学镀液的最佳工艺配方和条件: 粗化溶液浓度350 g/L, 粗化温度25 ℃, 粗化时间25 min; PdCl₂浓度0.4 g/L, 活化温度30 ℃, 活化时间5 min; 施镀温度 38~43 ℃, 施镀时间8~10 min, 镀液pH 8.5~9.5。利用SEM、EDS和XRD研究镀层的形貌、成分及镀层结构, 采用热震实验和极化曲线测试镀层的结合力及耐蚀性。结果表明: 最佳实验条件下获得的镀层均一性良好, 具有较好的耐腐蚀性能及较强的结合力。     

磁控溅射金刚石微粉表面镀镍及其在电镀金刚石线锯上的应用

为了增强电镀金刚石线锯对金刚石颗粒的把持力,提高金刚石线锯寿命,使金刚石颗粒与电镀金属更好地结合,通过振动辅助的摇摆式样品台将金刚石微粉充分分散,使用磁控溅射方法在粒径为30~40μm的金刚石微粉表面镀附金属镍,利用其制备电镀金刚石线锯,借助光学显微镜(OM)、扫描电子显微镜(SEM)、能量色散X射线光谱仪(EDX)对制备的镀镍金刚石颗粒和电镀金刚石线锯进行形貌和成分表征,研究不同溅射和电镀条件对实验结果的影响;通过自制切割实验装置对比金属化前后的金刚石微粉制备的金刚石线锯对金刚石的把持力。结果表明:磁控溅射表面镀镍能增强电镀金属与金刚石表面的结合,切割试验后金刚石脱落量相比原始金刚石明显从17.4%减少到4.9%。     

An exploratory study of a neural network approach for reliability data analysis

The results of this paper show that neural networks could be a very promising tool for reliability data analysis. Identifying the underlying distribution of a set of failure data and estimating its distribution parameters are necessary in reliability engineering studies. In general, either a chi-square or a non-parametric goodness-of-fit test is used in the distribution identification process which includes the pattern interpretation of the failure data histograms. However, those procedures can guarantee neither an accurate distribution identification nor a robust parameter estimation when small data samples are available. Basically, the graphical approach of distribution fitting is a pattern recognition problem and parameter estimation is a classification problem where neural networks have been proved to be a suitable tool. This paper presents an exploratory study of a neural network approach, validated by simulated experiments, for analysing small-sample reliability data. A counter-propagation network is used in classifying normal, uniform, exponential and Weibull distributions. A back-propagation network is used in the parameter estimation of a two-parameter Weibull distribution.     

Formation of TiO2 Modified Film on Carbon Steel

A new technique for preparing TiO2 modified film on carbon steel was accomplished by electroless plating and sol-gel composite process. The artificial neural network was applied to optimize the preparing condition of TiO2 modified film. The optimized condition for forming TiO2 modified film on carbon steel was that NiP plating for 50 min, dip-coating times as 4, heat treatment time for 2 h, and the molar ratio of complexing agent and Ti(OC4HZ9)4 kept 1.5:1. The results showed that TiO2 modified film have good corrosion resistance. The result conformed that it is feasible to design the preparing conditions of TiO2 modified film by artificial neural network.     

Einfluss der Netztopologie der künstlich neuronalen Netze auf das Ergebnis der Abschätzung zyklischer Kennwerte

Several estimation methods have been developed to estimate the cyclic material parameters out of the static material properties. Most of these methods are based on empirical equations. Increasing numbers of input‐ and influencing parameters lead to an rising effort for determining these equations and the accuracy decreases. For this reason new suitable methods are sought to estimate the cyclic material behaviour. A very promising approach is the application of the artificial neural networks, which can derive self‐depended a relationship between in‐ and output parameters. Static parameters such as yield strength, tensile strength …? etc., which can rapidly be determined used as input parameters. The output parameters are the cyclic material parameters of the strain‐life curve and stress‐strain curve according to the Manson‐Coffin‐Basquin‐ and Ramberg‐Osgood curve. Many different artificial neural networks with different structures and complexity can be applied. In this paper the influence of the topology of an artificial neural network on the estimation accuracy will be investigated. Based on the results of a reference artificial neural network it will be shown, that more complex topologies in the network do not lead inevitably to better estimations.     

多维线性模型参数估计的神经网络方法

本文提出一种用于多维线性模型（ＡＲ，ＡＲＭＡ）参数估计的神经网络方法和相应的递归预测误差算法。本文在分析多输入、单输出，含一个隐含和多层神经网络的输入输出关系的基础上，提出了首先将理想输出Ｘｉ进行预畸变（Ｆ（Ｘｔ））作为神经网络的训练目标。当神经网络训练完毕后，网络的连接权就是待估计的线性模型参数。本文提出方法的优点是网络结构简单，估计结果准确。仿真模拟结果表明，本文所提出的神经网络方法估计多维线性模型参数是有效的。     

Evolutionary Neural Network Modeling of Blast Furnace Burden Distribution

A neural network-based model of the burden layer thickness in the blast furnace is presented. The model is based on layer thicknesses estimates from a single radar measurement of the burden (stock) level in the furnace and describes the dependence between the layer thickness and key charging variables. An evolutionary algorithm is applied to train the network weights and connectivity by optimizing the model structure and parameters simultaneously, tackling part of the parameter estimation by linear least squares. This enhances convergence and results in parsimonious and transparent network models with actions that can be explained. Finally, the networks are used in a hybrid model for analyzing novel charging programs and for studying the limits of the charging process.     

Optimizing the parameters of multilayered feedforward neural networks through Taguchi design of experiments

The size and training parameters of artificial neural networks have a critical effect on their performance. This paper presents the application of the Taguchi Design of Experiments (DoEs) off‐line quality control method in the optimization of the design parameters of a neural network. Being a ‘parallel’ approach, the method offers considerable benefits in time and accuracy when compared with the conventional serial approach of trial and error. The use of the Taguchi method ensures that the quality of the neural network is taken into account at the design stage. The interpretation of the experimental results is based on the statistical technique known as analysis of variance (ANOVA). The signal‐to‐noise ratio (S/N) is used in designing a robust neural network that is less sensitive to noise. The effect of design parameters and neural network behaviour are also revealed as a result. Although a Wood Veneer Inspection Neural Network (WVINN) is the particular application presented here, the design methodology can be applied to neural networks in general. Copyright © 2000 John Wiley & Sons, Ltd.     

Application of fuzzy neural network to laser bending process of sheet metal

Abstract

The optimisation and selection of process plans is very important for laser bending of sheet metal to achieve the anticipated bending deformation. In this paper, an adaptive fuzzy neural network has been proposed to predict the bending deformation. This network integrates the learning power of neural networks with fuzzy inference systems. During the establishing process of the energy density (composed of three process parameters: laser power, scanning velocity, and spot diameter), width, thickness of sheet, and scanning path curvature were taken as four input variables of the network. The gradient descent learning algorithm was applied to optimally adjust the weight coefficients of the neural network and the parameters of the fuzzy membership functions. Then, the trained network was used to predict the laser bending deformation. Good correlation was found between the predictive and experimental results.     

基于插值与神经网络的包装薄膜热封参数分析

目的研究塑料薄膜热封工艺中热封参数之间的非线性关系,建立一种可用于自动包装机热封过程的数学模型。方法通过实验采集样本数据,并用附加动量法训练BP神经网络,建立热封参数之间的非线性数学模型,最后通过神经网络预测热封时间,并采用插值算法建立目标热封强度下热封温度和热封时间之间的多项式数学模型。结果通过插值算法与神经网络的结合运用,较为精确地描述了热封温度和热封时间之间的数学关系,插值函数实现了神经网络模型的简化,两者误差较小。结论通过文中方法确定了包装材料热封参数之间的非线性关系,将其用于热封包装设备,可提高设备的智能化程度。     

基于RBF神经网络的诺西肽发酵过程菌体质量浓度软测量

针对诺西肽发酵过程中菌体质量浓度的估计问题,提出了一种基于RBF神经网络的软测量建模方法.在诺西肽发酵过程非结构模型的基础上,根据隐函数存在定理确定出辅助变量,从而使其选择有严格的理论依据;根据每批样本数据对被预测对象的预估能力,自适应地为各个批次的训练样本分配权值,并进而实施加权RBF神经网络建模.实际应用表明,所提出的软测量建模方法是有效的.