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.     

Modular artificial neural network for prediction of petrophysicalproperties from well log data

An application of Kohonen's self-organizing map (SOM), learning-vector quantization (LVQ) algorithms, and commonly used backpropagation neural network (BPNN) to predict petrophysical properties obtained from well-log data are presented. A modular, artificial neural network (ANN) comprising a complex network made up from a number of subnetworks is introduced. In this approach, the SOM algorithm is applied first to classify the well-log data into a predefined number of classes, This gives an indication of the lithology in the well. The classes obtained from SOM are then appended back to the training input logs for the training of supervised LVQ. After training, LVQ can be used to classify any unknown input logs. A set of BPNN that corresponds to different classes is then trained. Once the network is trained, it is then used as the classification and prediction model for subsequent input data. Results obtained from example studies using the proposed method have shown to be fast and accurate as compared to a single BPNN network     

基于灰色 Elman 神经网络的爆破飞石距离预测研究

针对单一神经网络预测方法存在一些不足,将建立灰色关联分析法与 Elman 神经网络的耦合模型,对爆破飞石最大飞散距离进行预测研究。首先,利用灰色关联分析方法对数据进行预处理,确定各影响因素与爆破飞石距离之间的关联度;然后,根据关联度的大小,选择关联度较大的影响因素作为 Elman 神经网络的输入层数据;最后,用神经网络的功能对数据进行训练和预测。研究结果表明：利用灰色关联分析方法确定主要影响因素作为输入层,比单一使用 Elman 神经网络的预测精度更高,达到95％以上。     

Giant Magnetoimpedance Effect: Concept and Prediction in Amorphous Materials

Giant magneto impedance (GMI) effect was experimentally measured on as-cast, post-production and coated with chemical technique amorphous wire and ribbon materials consisted of varied chemical composition over a frequency range from 0.1 to 8 MHz under a static magnetic field between ?8 and +8 kA/m. The results show that each amorphous sample has a certain operational frequency for which the GMI effect has maximum magnitude and the other parameters such as annealing and coating have a significant influence on the GMI effect. It is believed that the domain structure and wall mechanism in the material are responsible for this behaviour. A 3-node input layer, 1-node output layer artificial neural network (ANN) model with three hidden layers including 30 neurons and full connectivity between the nodes was developed. A total of 1600 input vectors obtained from varied treated samples was available in the training data set. After the network was trained, better results were obtained from the network formed by the hyperbolic tangent transfer function in the hidden layers, there was a sigmoid transfer function in the output layer and we predicted the GMI. Comparing the predicted values obtained from the ANN model with the experimental data indicates that a well-trained neural network model provides very accurate results.     

Improved neural network model for reverse engineering

While conventional engineering transforms engineering concepts into real parts, in reverse engineering real parts are transformed into engineering models. The construction of a surface from three-dimensional (3D) measuring data points is an important problem in reverse engineering. This paper presents a reconstruction method for the sculptured surfaces from the 3D measuring data points. The surface reconstruction scheme is presented based on a neural network. The reconstruction of the existing surfaces is realized by training the network. A series of measuring points from existing sculptured surfaces is used as a training set. Once the neural network has been trained, it serves as a geometric model to generate all the points that are needed. However, the learning rate for the neural network is relatively slow, and the learning accuracy is often unacceptably low. In this paper, to improve the performance of the neural network, a pre-processor is proposed before the input layer. The pre-processor maps the input into the larger space by generating a set of linearly independent values. The effect of the pre-processor is to increase modelling accuracy, and reduce learning time. Based on this method, experimental results are given to show that the reconstructed surfaces are faithful to the original data points. The proposed scheme is useful for regular or irregular digitized data.     

基于全连接神经网络的压铸件质量预测算法

目的 针对复杂压铸制造过程中高精度监控和质量预测问题,构建全连接神经网络,以提高压铸件缺陷分类和预测的准确性及高效性。方法 提出了一种基于全连接神经网络的算法,用于压铸件的质量预测。以汽车发动机下缸体为研究对象,先通过压铸岛采集关键工艺数据,后通过异常值处理和数据归一化进行数据预处理,再采用最小冗余和最大相关性的启发式算法（MRMR）进行特征处理,选出对压铸件质量影响较大的5个参数,该算法以3个压射速度、真空度、动模流量为输入层参数,以铸件质量为输出层参数。最后确定该算法的结构及各个参数,进行模型的训练与构建,并与不同算法进行性能比较。结果 与传统的决策树、SVM算法相比,该算法在相同数据集的分类和预测性能方面均更优,表明全连接神经网络在预测压铸缺陷方面具有优势。结论 该算法在实际应用中具有很大的潜力,证明全连接神经网络在预测能力和精度方面具有优势,可以为数据分类和预测提供更好的解决方案。     

Robustness analysis of radial base function and multi-layered feed-forward neural network models

In this paper, two popular types of neural network models (radial base function (RBF) and multi-layered feed-forward (MLF) networks) trained by the generalized delta rule, are tested on their robustness to random errors in input space. A method is proposed to estimate the sensitivity of network outputs to the amplitude of random errors in the input space, sampled from known normal distributions. An additional parameter can be extracted to give a general indication about the bias on the network predictions. The modelling performances of MLF and RBF neural networks have been tested on a variety of simulated function approximation problems. Since the results of the proposed validation method strongly depend on the configuration of the networks and the data used, little can be said about robustness as an intrinsic quality of the neural network model. However, given a data set where ‘pure’ errors from input and output space are specified, the method can be applied to select a neural network model which optimally approximates the nonlinear relations between objects in input and output space. The proposed method has been applied to a nonlinear modelling problem from industrial chemical practice. Since MLF and RBF networks are based on different concepts from biological neural processes, a brief theoretical introduction is given.     

分子动力学和人工神经网络的算法耦合研究

对比了分子动力学和人工神经网络两种不同模拟算法的主要特点,提出了将这两种算法相互耦合,即将分子动力学的模拟结果作为人工神经网络的训练样本,训练后的人工神经网络用来预测.利用分子动力学建立了金刚石表面化学气相沉淀的模型,运用两种算法的耦合计算了碳原子在金刚石表面吸收概率,解吸收概率和散射概率.计算表明,这两种算法的耦合可节省计算资源,同时保证了一定的精确度.     

Calibration and verification of DEM parameters for dynamic particle flow conditions using a backpropagation neural network

The Discrete Element Method (DEM) requires input parameters to be calibrated and validated in order to accurately model the physical process being simulated. This is typically achieved through experiments that examine the macroscopic behavior of particles, however, it is often difficult to efficiently and accurately obtain a representative parameter set. In this study, a method is presented to identify and select a set of DEM input parameters by applying a backpropagation (BP) neural network to establish the non-linear relationship between dynamic macroscopic particle properties and DEM parameters. Once developed and trained, the BP neural network provides an efficient and accurate method to select the DEM parameter set. The BP neural network can be developed and trained for one or more laboratory calibration experiments, and be applied to a wide range of bulk materials under dynamic flow conditions.     

Analog fault diagnosis of actual circuits using neural networks

We have developed a neural-network based analog fault diagnostic system for actual circuits. Our system uses a data acquisition board to excite a circuit with an impulse and sample its output to collect training data for the neural network. The collected data is preprocessed by wavelet decomposition, normalization, and principal component analysis (PCA) to generate optimal features for training the neural network. This ensures a simple architecture for the neural network and minimizes the size of the training set required for its proper training. Our studies indicate that features extracted from actual circuits lie closer to each other and exhibit more overlap across fault classes compared to SPICE simulations. This implies that the neural network architecture which can most reliably perform fault diagnosis of actual circuits is one whose outputs estimate the probabilities that input features belong to different fault classes. Our work also shows that SPICE simulations can be used to select appropriate features for training the neural network. Reliable diagnosis of faults in an actual circuit, however, requires training data from the circuit itself. Our fault diagnostic system, trained and tested using data obtained from real sample circuits, achieves 95% accuracy in classifying faulty components     

Identification of Materials Properties with the Help of Miniature Shear Punch Test Using Finite Element Method and Neural Networks

This paper describes an approach to identify the mechanical properties i.e. fracture and yield strength of steels. The study involves the FE simulation of shear punch test for various miniature specimens thickness ranging from 0.20mm to 0.80mm for four different steels using ABAQUS code. The experimental method of the miniature shear punch test is used to determine the material response under quasi-static loading. The load vs. displacement curves obtained from the FE simulation miniature disk specimens are compared with the experimental data obtained and found in good agreement. The resulting data from the load vs. displacement diagrams of different steels specimens are used to train the neural networks to predict the properties of materials i.e. fracture and yield strength. Two different feed forward neural networks have been created and trained in order to predict the Fracture toughness and yield strength values of different steels. L-M algorithm has been used in the networks to form an output function corresponding to the input vectors used in the network. The trained network provides the output values i.e., fracture toughness and yield strength of unknown input values, which are within in the range of data that is used for the training of network.     

Understanding weld modelling processes using a combination of trained neural networks

In this paper, a set of neural networks has been trained for weld modelling processes with different architecture and training parameters. The set of neural networks is trained using actual weld data available in the literature. The performance of each neural network in this set is defined by two performance measures of interest, namely training error and generalization error. Instead of using one of the best networks from this set of trained networks, a method of combining the outputs of all the network from the set is proposed and is called the combined output (or output of the combined network). It is shown that the performance measures of interest obtained using this combined output is better than the performance measures of interest obtained by all the individual neural networks in the set.     

Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network

In this study, the prediction of flow stress in 304 stainless steel using artificial neural networks (ANN) has been investigated. Experimental data earlier deduced—by [S. Venugopal et al., Optimization of cold and warm workability in 304 stainless steel using instability maps, Metall. Trans. A 27A (1996) 126–199]—were collected to obtain training and test data. Temperature, strain-rate and strain were used as input layer, while the output was flow stress. The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. The results of this investigation shows that the R² values for the test and training data set are about 0.9791 and 0.9871, respectively, and the smallest mean absolute error is 14.235. With these results, we believe that the ANN can be used for prediction of flow stress as an accurate method in 304 stainless steel.     

基于多传感器信号和卷积神经网络的滚动轴承故障诊断

针对滚动轴承故障信号非平稳非线性且易受背景噪声干扰的特点,结合深度学习的优势,提出了一种基于卷积神经网络(CNN)的滚动轴承故障诊断法。将不同故障下多个传感器测得的1维(1D)振动信号转化为2维(2D)灰度图像作为网络输入,并将其分为训练集和测试集;将训练集输入卷积神经网络进行训练,自动提取其中的特征;测试集被用于验证学习完毕的网络的有效性,实现滚动轴承故障识别。该方法不依赖于人为经验和信号处理技术进行预先的信号特征提取,实验数据分析表明,相比于经典的支持向量机和概率神经网络方法,提出的方法识别准确率更高且更稳定。     

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。     

A neural network method for restoring the initial impurity concentration distribution from data of ion sputter depth profiling

A new approach to solving the problem of restoring the initial impurity concentration distribution from data of ion sputter depth profiling is proposed. The algorithm of impurity profile restoration is based on using an artificial neural network with the input signals representing surface concentrations of impurity determined at sequential moments of sputter depth profiling. The artificial neural network is trained for various depths and thicknesses of the impurity-containing layer and various values of parameters of the adopted model equation of diffusion-like ion mixing.     

基于BP神经网络算法的316L不锈钢极薄带热处理力学性能预测

目的 为了预测不锈钢极薄带热处理后的力学性能、优化热处理工艺以及实现热处理工艺的智能控制,构建基于BP算法的神经网络模型。方法 以316L不锈钢极薄带为研究对象,进行热处理试验和拉伸试验,通过以热处理的退火温度、保温时间和取样方向作为输入层参数,以屈服强度、抗拉强度、断后伸长率作为输出层参数,采用BP算法构建了316L不锈钢极薄带力学性能预测的思维进化算法优化BP神经网络模型,并进行模型的预测和应用验证,考虑不同隐含层节点数及不同BP神经网络模型对性能的影响。结果 思维进化算法优化的BP神经网络模型测试集的屈服强度、抗拉强度和断后伸长率的平均相对误差分别为8.92%、5.21%和9.28%,训练集相关系数为0.980 94。思维进化算法优化BP网络单、双隐含层误差总和最低分别为0.578 6和0.546 9,BP网络与思维进化算法优化的BP网络误差总和最低分别为0.579 9和0.546 9。结论 思维进化算法优化BP神经网络模型具有较好的预测能力和泛化能力,以及较高的预测精度。与企业现用生产工艺相比,采用模型优化后热处理工艺的综合力学性能有显著提高。     

基于GADF-CNN的滚动轴承故障诊断方法

针对一维信号作为卷积神经网络输入时无法充分利用数据间的相关信息的问题,提出GADF-CNN的轴承故障诊断模型。利用格拉姆角差域(GADF)对采集到的振动信号进行编码,可以很容易地进行角度透视,从而识别出不同时间间隔内的时间相关性并生产相应特征图,之后将其输入卷积神经网络(CNN)自适应的完成滚动轴承故障特征的提取与分类。为了验证模型性能,采用凯斯西储大学轴承数据集进行轴承故障诊断分析,同时引入常见神经网络作为对比,检验不同模型的分类性能。结果表明,相较于其他图像编码方式与神经网络,该模型在载荷变化以及噪声污染时,仍保持了良好的诊断性能。     

Machine and component residual life estimation through the application of neural networks

This paper concerns the use of neural networks for predicting the residual life of machines and components. In addition, the advantage of using condition-monitoring data to enhance the predictive capability of these neural networks was also investigated. A number of neural network variations were trained and tested with the data of two different reliability-related datasets. The first dataset represents the renewal case where the failed unit is repaired and restored to a good-as-new condition. Data were collected in the laboratory by subjecting a series of similar test pieces to fatigue loading with a hydraulic actuator. The average prediction error of the various neural networks being compared varied from 431 to 841 s on this dataset, where test pieces had a characteristic life of 8971 s. The second dataset were collected from a group of pumps used to circulate a water and magnetite solution within a plant. The data therefore originated from a repaired system affected by reliability degradation. When optimized, the multi-layer perceptron neural networks trained with the Levenberg-Marquardt algorithm and the general regression neural network produced a sum-of-squares error within 11.1% of each other for the renewal dataset. The small number of inputs and poorly mapped input space on the second dataset meant that much larger errors were recorded on some of the test data. The potential for using neural networks for residual life prediction and the advantage of incorporating condition-based data into the model was nevertheless proven for both examples.     

Modeling of materials with fading memory using neural networks

A neural network‐based concept for the solution of a fractional differential equation is presented in this paper. Fractional differential equations are used to model the behavior of rheological materials that exhibit special load (stress) history characteristics (e.g. fading memory). The new concept focuses on rheological materials that exhibit Newtonian‐like displacement behavior when undergoing (time varying) creep loads. For this purpose, a partial recurrent artificial neural network is developed. The network supersedes the storage of the entire load (stress) history in contrast to the exact solution of the fractional differential equation, where access to all previous load (stress) increments is required to determine the new displacement (strain) increment. The network is trained using data obtained from six different creep simulations. These creep simulations have been conducted by means of the exact solution of the fractional differential equation, which is also included in the paper. Furthermore, the network architecture as well as a complete set of network parameters is given. A validation of the network has been carried out and its outcome is discussed in the paper. To illustrate the particular way the network works, all relevant algorithms (e.g. scaling of the input data, data processing, transformation of the output signal, etc.) are provided to the reader in this paper. Copyright © 2008 John Wiley & Sons, Ltd.