Double Glow Plasma Surface Alloying Process Modeling Using Artificial Neural Networks

A model is developed for predicting the correlation between processing parameters and the technical target of double glow by applying artificial neural network (ANN). The input parameters of the neural network (NN) are source voltage, workpiece voltage, working pressure and distance between source electrode and workpiece. The output of the NN model is three important technical targets, namely the gross element content, the thickness of surface alloying layer and the absorpticm rate (the ratio of the mass loss of source materials to the increasing mass of workpiece) in the processing of double glow plasma surface alloying. The processing parameters and technical target are then used as a training set for an artificial neural network. The model is based on multiplayer feedforward neural network. A very good performance of the neural network is achieved and the calculated results are in good agreement with the experimental ones.     

Artificial Neural Networks: Comparison of Two Programs for Modeling a Process of Nanoparticle Preparation

ABSTRACT

Artificial Neural Networks (ANNs) were used to predict nanoparticle size and micropore surface area of polylactic acid nanoparticles, prepared by a double emulsion method. Different batches were prepared while varying polymer and surfactant concentration, as well as homogenization pressure. Two commercial ANNs programs were evaluated: Neuroshell® Predictor, a black-box software adopting both neural and genetic strategies, and Neurosolutions®, allowing a step-by-step building of the network. Results were compared to those obtained by statistical method. Predictions from ANNs were more accurate than those calculated using non-linear regression. Neuroshell® Predictor allowed quantification of the relative importance of the inputs. Furthermore, by varying the network topology and parameters using Neurosolutions®, it was possible to obtain output values which were closer to experimental values. Therefore, ANNs represent a promising tool for the analysis of processes involving preparation of polymeric carriers and for prediction of their physical properties.     

神经网络方法在自相关过程控制中的应用

将传统休哈特控制图应用于自相关过程控制时,会引发大量虚发报警.本文将使用时间序列模型模拟自相关过程并将神经网络方法引入自相关过程控制中.以神经网络特有的模式识别技术,对自相关过程中均值发生突变的情况进行监控,取得了良好效果.     

一个新的超塑变形内部损伤预测模型

超塑变形往往具有空洞敏感性，对空洞的研究引起国内外学者的重视并取得较大进展，但现有描述超塑变形时空洞损伤行为的力学模型普遍存在精度问题，利用神经网络对超塑变形时的空洞损伤程度进行预测，不仅可提高精度，同时亦能充分反映超塑变形工艺参数对损伤的影响规律。因此，这就为研究超塑变形时的空洞损伤提供了一种新方法，同时也为神经网络的应用开辟了一个新领域。     

多位姿焊接焊缝成形尺寸的神经网络建模

根据焊缝成形尺寸受焊件位姿、焊接条件和焊接规范等多种因素的影响，利用人工神经网络－ＢＰ网络的特性，探讨了建立这种多输入多输出非线性系统静态模型的方法，并通过一个实例检验了这种方法是有效的，并具有较高的精度和速度。     

Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks

Most multivariate quality control procedures evaluate the in‐control or out‐of‐control condition based upon an overall statistic, like Hotelling's T². Although T² is optimal for finding a general shift in mean vectors, it is not optimal for shifts that occur for some subset of variables. This introduces a persistent problem in multivariate control charts, namely the interpretation of a signal that often discourages practitioners in applying them. In this paper, we propose an artificial neural network based model to diagnose faults in out‐of‐control conditions and to help identify aberrant variables when Shewhart‐type multivariate control charts based on Hotelling's T² are used. The results of the model implementation on two numerical examples and one case of real world data are encouraging. Copyright © 2005 John Wiley & Sons, Ltd.     

Numerical Modeling of Heat-Affected Zone in the GMAW Process

Materials Science - Low-carbon steel St37-2 is widely used in the bus-production industry. The gas-metal arc welding (GMAW) is strongly applied to join steel components due to its ease and low...     

Predicting the Fatigue Life of Different Composite Materials Using Artificial Neural Networks

Artificial Neural Networks (ANN) have been recently used in modeling the mechanical behavior of fiber-reinforced composite materials including fatigue behavior. The use of ANN in predicting fatigue failure in composites would be of great value if one could predict the failure of materials other than those used for training the network. This would allow developers of new materials to estimate in advance the fatigue properties of their material. In this work, experimental fatigue data obtained for certain fiber-reinforced composite materials is used to predict the cyclic behavior of a composite made of a different material. The effect of the neural network architecture and the training function used were also investigated. In general, ANN provided accurate fatigue life prediction for materials not used in training the network when compared to experimentally measured results.     

BP神经网络对陶瓷材料烧结性能预测的研究

用C＋＋语言建立了BP神经网络模型，并用TZP陶瓷材料的烧结性能数据进行训练及预测。预测结果表明，BP神经网络可以用于陶瓷材料的浇结性能的预测，并且精度较高。     

Optimization of Short Load Forecasting in Electricity Market of Iran Using Artificial Neural Networks

Accurate short-term load forecasting (STLF) is one of the essential requirements for power systems. In this paper, two different seasonal artificial neural networks (ANNs) are designed and compared in terms of model complexity, robustness, and forecasting accuracy. Furthermore, the performance of ANN partitioning is evaluated. The first model is a daily forecasting model which is used for forecasting hourly load of the next day. The second model is composed of 24 sub-networks which are used for forecasting hourly load of the next day. In fact, the second model is partitioning of the first model. Time, temperature, and historical loads are taken as inputs for ANN models. The neural network models are based on feed-forward back propagation which are trained and tested using data from electricity market of Iran during 2003 to 2005. Results show a good correlation between actual data and ANN outcomes. Moreover, it is shown that the first designed model consisting of single ANN is more appropriate than the second model consisting of 24 distinct ANNs. Finally ANN results are compared to conventional regression models. It is observed that in most cases ANN models are superior to regression models in terms of mean absolute percentage error (MAPE).     

Artificial Neural Networks in Mechanical Fault Diagnosis

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人工神经网络和机械故障诊断

智能化诊断是现代故障诊断技术发展的主要趋势,人工神经网络技术的出现为这种智能化提供了一个全新的途径。本文首先简单介绍了人工神经网络的基本性能及几个重要模型,着重探讨了人工神经网络技术在机械故障诊断领域中预测与控制、工况监测与故障分类诊断、模糊诊断和基于专家系统的故障诊断等几个主要方面的应用,指出人工神经网络技术与现有的信号处理、模式识别、模糊逻辑、专家系统等技术相结合,以解决故障信号分析与处理、故障模式识别以及故障论域专家知识的组织和推理等问题,必将加快智能化诊断发展的进程。可以预料:基于人工神经网络的故障诊断技术将具有广阔的发展与应用前景,并且随着VLsI 技术的发展,这一新技术必将广泛地应用于各种诊断实例。最后讨论了进一步值得研究的方向。     

Predicting the Crushing Behavior of Axially Loaded Elliptical Composite Tubes Using Artificial Neural Networks

In this research work, the artificial neural networks (ANN) technique is used in predicting the crushing behavior and energy absorption characteristics of axially-loaded glass fiber/epoxy composite elliptical tubes. Predictions are compared to actual experimental results obtained from the literature and are shown to be in good agreement. Effects of parameters such as network architecture, number of hidden layers and number of neurons per hidden layer are also considered. The study shows that ANN techniques can effectively be used to predict the crushing response and the energy absorption characteristics of elliptical composite tubes with various ellipticity ratios subjected to axial loading.     

人工神经网络应用于适应滤波的研究

目的 用神经网络实现Adaline自适应滤波权值调节。方法 在Adaline自适应滤波器中,滤波器的权值调节是通过LMS自适应算法进行调整的,但LMS的自适应算法的收敛速度慢,从而大大影响了Adaline自适应滤波器的滤波性能,作者利用TH网络进行TH－Adaline滤波器系统辨识。并给出了Adaline滤波器权值输出变化曲线的仿真结果。结果与结论 利用TH神经网络来实现滤波器权值调节的Adaline自适应滤波器运算速度比运用LMS算法的Adaline自适应滤波器的收敛速度快。     

Heat Capacity of Ionic Liquids Using Artificial Neural Networks and the Concept of Mass Connectivity

Artificial neural networks and the concept of mass connectivity index are used to correlate and predict the heat capacity at constant pressure of ionic liquids (ILs). Different topologies of a multilayer feed-forward artificial neural network were studied, and the optimum architecture was determined. Heat-capacity data at several temperatures taken from the literature for 31 ILs with 477 data points were used for training the network. To discriminate among the different substances, the molecular mass of the anion and of the cation and the mass connectivity index were considered as the independent variables. The capabilities of the designed network were tested by predicting heat capacities for situations not considered during the training process (65 heat-capacity data for nine ILs). The results demonstrate that the chosen network and the variables considered allow estimating the heat capacity of ILs with acceptable accuracy for engineering calculations. The program codes and the necessary input files to calculate the mass connectivity index and the heat capacity for other ILs are provided.     

基于人工神经网络的结构损伤识别方法

根据观测的结构频率变化,建立了基于人工神经网络的悬臂梁结构损伤识别方法.把结构固有频率的变化率作为BP神经网络的输入参数,对悬臂梁结构模型进行了损伤数值模拟计算.为了提高神经网络的泛化推广能力和收敛速度,将BFGS优化方法应用到神经网络的训练过程中.数值计算结果表明,所建立的结构损伤识别方法具有收敛速度快、识别精度高等特性.     

人工神经网络在玻璃配方设计中的应用研究

应用人工神经网络技术,采用Neuralworks Predict软件建立BP网络模型,通过对R2O-MO-Al2O3-SiO2系统玻璃组成与热膨胀系数关系实验数据的训练,以期能预测该系统指定组成的玻璃的热膨胀系数?研究结果表明,所建立的神经网络模型能较正确地反映玻璃氧化物组成与其热膨胀系数之间的规律性。模型对给定组成玻璃热膨胀系数的预测值与实际测试值的相对误差在6．4％以内,表明由神经网络技术建立的这一模型能正确反映R2O-MO-Al2O3-SiO2系统玻璃组成与热膨胀系数间的内在规律性。     

基于人工神经网络的光谱反射率重建

目的研究基于BP神经网络法和FNN神经网络法重构图像光谱反射率的精度。方法以SG标准色卡作为训练样本,分别使用BP和FNN神经网络法,对测试样本DC标准色卡的光谱反射率进行预测,并利用CIEL*a*b*色差公式、均方根误差(ERMS)和光谱匹配精度(GFC)对结果进行评价。结果 BP和FNN神经网络重构的光谱反射率平均色差(ΔEab)分别为2.997和3.071,平均均方根误差(ERMS)分别为0.056和0.049,平均光谱匹配精度(GFC)分别为0.987和0.991。结论 2种神经网络方法重构的光谱反射率具有相当优越的色度和光谱精度。相比于FNN神经网络,BP神经网络更加适合于光谱图像的获取领域。