烟气轮机涡轮盘高温合金剩余寿命预测

本文针对已运行60000h的Waspaloy合金烟气轮机涡轮盘进行剩余寿命预测分析,采用不同试验条件下得到的waspaloy合金持久寿命数据对人工神经网络模型进行训练,得到预测精度较高的模型参数,建立温度、应力等服役条件与持久断裂寿命之间的人工神经网络模型,并利用该模型对Waspaloy合金涡轮盘的剩余寿命进行预测分析。结果表明,中间层节点个数为15时,所建立的人工神经网络模型对Waspaloy合金的持久断裂寿命具有最好的统计预测精度,并可以良好地表征Waspaloy合金剩余持久寿命与服役条件间复杂的非线性关系。     

基于神经元网络的滚动轴承诊断系统设计

通过对轴承的振动特征的分析，确定了基于故障的征兆频率．进而又构造出了轴承的征兆空间和故障空间的模式；采用多层前馈型神经网络．通过网络的自学习和训练，实现了两个空间之间的非线性映射；最后，完成了轴承运行状态的智能化监测诊断。     

基于人工神经网络技术的铸造过程数值模拟优化研究

以凸轮轴的铸造过程为例,将人工神经网络技术应用于铸造数值模拟优化中。设计了7个热电偶测试点,分别通过三维有限元模拟方法和实际测量方法得到了对应点的温度场分布数据。以这些数据为样本,运用神经网络进行了充型凝固数值模拟的优化。通过比较发现,神经网络优化处理后的数值模拟有效的提高了模拟效率和精度,将神经网络优化引入到铸造过程模拟中具有良好的可行性。     

Modeling of Thermal Expansion Coefficients of Ni-Based Superalloys Using Artificial Neural Network

The objective of this work is to model the thermal expansion coefficients of various Ni-based superalloys used in gas turbine components. The thermal expansion coefficient is described as a function of temperature, chemical composition including Ni, Cr, Co, Mo, W, Ta, Nb, Al, Ti, B, Zr, and C contents as well as heat treatment including solutionizing and aging. Experimental values are well described and their relative changes well correlated by the model. Because gas turbine engine components operate under severe loading conditions and at high and varying temperatures, the prediction of their thermal expansion coefficient is crucial. The model developed in this work can be useful for design optimizations for minimizing thermo-mechanical stresses between the base alloys and potential protective coatings or adjacent components. It can substantially contribute to improve the performance and service life of gas turbine components.     

基于神经网络的双金属复合弯管铸造过程的数值仿真

给出了双金属复合管的铸造工艺,研究了人工神经网络技术在铸造数值仿真优化中的应用,人工神经网络采用基于自适应学习率-动量项的误差反向传播梯度下降算法。用热电偶对双金属复合管铸造温度场进行了实测,并以温度场实测数据为样本,仿真了双金属复合管充型凝固过程的温度分布。通过实测数据与仿真数据的比较,神经网络优化处理后仿真的最大相对误差为2.1%。铸造过程的仿真为双金属复合管的设计和工艺制订提供了理论依据。     

CO2焊接工艺参数优化的人工神经网络设计

比较了在虚拟仪器开发软件LabVIEW编程基础上用多种方法实现BP神经网络算法的优劣,并利用LabVIEW编程环境提供的CIN(Call Library Function Node)节点调用C语言编程代码.建立改进算法的多层BP神经网络,以焊接电流、电弧电压、焊接速度、施焊角度为输入层,焊缝熔宽、焊缝熔深为输出层.实验证明,利用该神经网络模型,可预测焊缝成形,并根据需要,选择CO2焊接工艺参数,在实际应用中取得较好的效果.     

人工神经网络在PLC控制系统故障诊断中的应用

详细介绍了一种新的PLC控制系统故障诊断方法。随着PLC应用越来越广泛，研究PLC控制系统故障诊断具有重大意义。文中首先对PLC控制程序结构、PLC控制系统故障进行了分析。然后将人工神经网络引入PLC控制系统故障诊断，提出了一种新的故障诊断方法。在文章最后，还给出了该故障诊断方法的应用实例。     

基于BP神经网络的TC11钛合金工艺-性能模型预测

材料工艺与性能的关系具有复杂、非线性交互等特点。本文根据TC11钛合金力学性能与其影响因素之间的映射关系,以大量的试验数据为基础,建立了BP神经网络模型。模型的输入包括锻造温度、锻后冷却方式等热加工工艺参数;输出为常用的力学性能指标,即抗拉强度、屈服强度、延伸率和断面收缩率。运用该模型对TC11钛合金力学性能进行了预测,并通过试验数据对模型的预测精度进行了可靠性验证。同时,运用已建立的神经网络模型对TC11钛合金工艺参数与力学性能的关系进行了分析。结果表明,所建立的力学性能预测模型具有良好的外推能力,并且可以很好地反映出该合金的工艺-性能之间的复杂关系。     

Prediction of Flow Stress in Cadmium Using Constitutive Equation and Artificial Neural Network Approach

A model is developed to predict the constitutive flow behavior of cadmium during compression test using artificial neural network (ANN). The inputs of the neural network are strain, strain rate, and temperature, whereas flow stress is the output. Experimental data obtained from compression tests in the temperature range ?30 to 70 °C, strain range 0.1 to 0.6, and strain rate range 10^?3 to 1 s^?1 are employed to develop the model. A three-layer feed-forward ANN is trained with Levenberg-Marquardt training algorithm. It has been shown that the developed ANN model can efficiently and accurately predict the deformation behavior of cadmium. This trained network could predict the flow stress better than a constitutive equation of the type $ \dot{\upvarepsilon } = A\sinh (\upalpha /\upsigma )^{n} \exp ( - Q/RT) $ .     

基于神经网络的数控加工物理仿真的研究

为描述数控加工过程的物理现象及其规律,提出数控加工物理仿真内容体系结构,综合考虑多种影响因素运用人工神经网络分别建立起刀具磨损、切削力、温度场、切屑形态和切削振动的仿真模型,该模型大大地提高了数控加工仿真的真实性.最后指出了物理仿真的发展趋势.     

基于BP神经网络Ti600合金本构关系模型的建立

运用Gleeble-1500热模拟机对Ti600合金的圆柱试样进行等温压缩变形试验,以试验所得数据（变形温度800~1100 ℃,应变速率0.01~10 s-1）为基础,基于BP神经网络方法建立了该合金的高温本构关系模型。结果表明：BP神经网络本构关系模型具有很高的预测精度,可以很好地描述Ti600合金在高温变形时各热力学参数之间高度非线性的复杂关系,为本构关系模型的建立提供了一种更加准确有效的方法。     

Prediction of Tensile Property of Hydrogenated Ti600 Titanium Alloy Using Artificial Neural Network

An artificial neural network (ANN) model has been developed to analyze and predict the correlation between tensile property and hydrogenation temperature and hydrogen content of hydrogenated Ti600 titanium alloy. The input parameters of the neural network model are hydrogenation temperature and hydrogen content. The output is ultimate tensile strength. The accuracy of ANN model was tested by the testing data samples. The prediction capability of ANN model was compared with the multiple linear regression approach and response surface method. The combined influence of inputs on the tensile property is also simulated using ANN model. It is found that excellent performance of the ANN model was achieved, and the results showed good agreement with experimental data. Moreover, the developed ANN model can be used as a tool to control the tensile property of titanium alloys.     

Modeling Microstructural Evolution During Dynamic Recrystallization of Alloy D9 Using Artificial Neural Network

An artificial neural network (ANN) model was developed to predict the microstructural evolution of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel (Alloy D9) during dynamic recrystallization (DRX). The input parameters were strain, strain rate, and temperature whereas microstructural features namely, %DRX and average grain size were the output parameters. The ANN was trained with the database obtained from various industrial scale metal-forming operations like forge hammer, hydraulic press, and rolling carried out in the temperature range 1173-1473 K to various strain levels. The performance of the model was evaluated using a wide variety of statistical indices and the predictability of the model was found to be good. The combined influence of temperature and strain on microstructural features has been simulated employing the developed model. The results were found to be consistent with the relevant fundamental metallurgical phenomena.

Optimization of LPDC Process Parameters Using the Combination of Artificial Neural Network and Genetic Algorithm Method

In this article, the low-pressure die-cast (LPDC) process parameters of aluminum alloy thin-walled component with permanent mold are optimized using a combining artificial neural network and genetic algorithm (ANN/GA) method. In this method, an ANN model combining learning vector quantization (LVQ) and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component with 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared and no obvious defects such as shrinkage, gas porosity, distortion, and crack were found in the component. The results indicate that the combining ANN/GA method is an effective tool for the process optimization of LPDC, and they also provide valuable reference on choosing the right process parameters for LPDC thin-walled aluminum alloy casting.     

Prediction of Composition of GaInAsSb Epilayers by MOCVD Using Pattern Recognition and Artificial Neural Network Method

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基于BP神经网络的开式冷挤压成形极限变形程度预测

采用实验研究与神经网络相结合的方法进行研究，通过大量实验，获取大量数据，在此基础上，建立BP网络模型。通过对开式冷挤压极限变形程度神经网络计算结果与实验结果的比较，其精度较高，证明用神经网络方法既可以实现开式冷挤压工艺的参数预测，叉能给出系统完整的可供指导实际生产的工艺参数数据，对于开式冷挤压的实际生产具有指导意义，是一种可行的分析手段。     

人工神经网络控制在焊接中的研究应用

介绍了近些年来国外关于人工神经网络控制技术在焊接过程的自动化、智能化控制中的研究应用。     

基于(RBF)神经网络的自动化电器设备故障诊断方法

为了对自动化电器设备的故障状态进行准确的识别,文章引入了(RBF)神经网络的故障诊断方法.并针对其不能学习新状态类型的缺陷,提出了一种改进的算法.并将该算法应用于电器设备的故障诊断,改进的算法除了能够对已知的状态进行准确的识别外,还能够发现并学习未纳入训练样本集的状态类型,从而具备了新状态类型的识别功能.     

铝青铜力学性能人工神经网络模型的建立和应用

采用MATLAB的BP人工神经网络,以铝青铜的化学成分作为输入参数,其抗拉强度σb、屈服强度σ0.2和伸长率δ作为输出,建立了高强度船用螺旋桨铝青铜的力学性能预测模型。计算结果表明,三项输出的预测值与实测数据接近,其相对误差基本在±2%～±7%,因此,该模型对高强度船用螺旋桨铝青铜的生产具有一定的指导意义。