三维磁场计算的有限元神经网络模型

针对有限元法计算量大的缺点,将神经网络的并行结构应用于有限元法的运算过程,提出一种求解三维磁场的有限元神经网络模型,实现了有限元法的并行分布式计算。采用共轭梯度法作为神经网络的训练算法对仿真模型进行分析,得到了磁场强度的矢量图、剖面的等磁动势线图。与有限元方法相比,三维有限元神经网络将复杂运算分布到各个神经网络节点并行求解,简化了有限元法的运算过程,是一种精确快速的磁场分析方法。

利用有限元神经网络计算漏磁场方法研究

针对有限元法计算量大的不足,用神经网络模拟有限元的分析过程,建立了求解漏磁场计算的有限元神经网络模型,并采用共轭梯度学习算法,对矩形缺陷的漏磁场进行了计算。通过计算得到了磁场强度、磁感应强度矢量图以及漏磁通密度x、y分量图。结果表明,有限元神经网络能够实现漏磁场的并行求解,具有速度快、稳定性好等优点,是一种漏磁场的快速计算方法。

漏磁检测中的缺陷重构方法

漏磁检测技术中的缺陷重构方法是目前漏磁检测缺陷评估的研究热点与难点。在对漏磁检测缺陷重构进行概述的基础上,综述了现有的缺陷重构方法:开环逆向重构法和闭环伪逆重构法,并对这两类方法中常用的逆向模型、前向模型、优化算法及其特点进行了较为详细的介绍。指出了基于数据融合技术、图像处理技术和多缺陷综合评估的缺陷重构方法的研究发展方向。

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

并行ENN-FEM分析力学参数对洞室群稳定性的影响

将洞室群视为一特定系统,采用并行进化神经网络有限元(ENN-FEM)方法建立该系统与岩体力学参数之间的相对应关系。采用遗传算法分析得到对围岩稳定性最不利的参数组合,对组合中任一参数可通过参数敏感度函数分析其敏感度,并由此综合评估各软岩力学参数对洞室群稳定性的影响,确定出关键岩层以给设计和施工提供指导性建议。实例计算表明该方法是合理的,且具有智能化和综合分析的优点。

库区桥梁悬臂浇筑中跨合龙施工风险分析

为评估三峡库区频发微震与周期性水流力对库区桥梁悬臂浇筑施工的合龙影响,采用有限元-正交试验设计-神经网络-蒙特卡罗模拟的联合施工风险评估方法对其进行分析.以正交试验设计法生成水流速度、水流高度、微震加速度的概率分布样本,耦合有限元模型与BP神经网络进行训练,建立各控制参数与中跨合龙中轴线偏移值之间的非线性映射关系,然后利用蒙特卡罗模拟随机生成影响参数,代入训练好的BP神经网络预测中轴线偏移误差风险.结果显示,水流力-高频微震耦合作用使合龙段中轴线偏移的风险概率已达到5.06%,库区大桥悬臂浇筑施工线型偏离风险需要引起足够的关注与防范.

A forming load analysis for extrusion process of AZ31 magnesium

The effect of extrusion parameters on the extrusion load for AZ31 magnesium alloy was investigated with the support of numerical methods.With this regard,the process temperature,extrusion ratio,friction factor and punch velocity were selected as main parameters for the experiments.Besides,the experimental results were analyzed by using the finite element method(FEM)and artificial neural network(ANN)method to build a numerical model for predicting the forming load.All the experimental and numerical results were compared to each other and it was concluded from the results that the effect of friction factor on the extrusion load is more dominant at lower extrusion temperature for all given extrusion ratios and punch velocities.Besides this,higher extrusion ratios require higher process temperatures to obtain the lower extrusion load.Also,it was observed that the increase in the extrusion speed causes a significant increase in the forming load for all extrusion ratios and extrusion temperatures.

Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973−1123 K and strain rates of 0.01−10 s^(−1).Strain-compensated Arrhenius(SCA)and back-propagation artificial neural network(BPANN)methods were selected to model the constitutive relationship,and the models were further evaluated by statistical analysis and cross-validation.The stress−strain data extended by two models were implanted into finite element to simulate hot compression test.The results indicate that the flow stress is sensitive to deformation temperature and strain rate,and increases with increasing strain rate and decreasing temperature.Both the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors,but the fitting accuracy of BPANN is higher than that of SCA.Sixteen cross-validation tests also confirm that the BPANN model has high prediction accuracy.Both models are effective and feasible in simulation,but BPANN model is superior in accuracy.

Backpropagation neural network method in data processing of ultrasonic imaging logging-while-drilling

The existing methods for extracting the arrival time and amplitude of ultrasonic echo cannot eff ectively avoid the local interference of ultrasonic signals while drilling,which leads to poor accuracy of the echo arrival time and amplitude extracted by an ultrasonic imaging logging-while-drilling tool.In this study,a demodulation algorithm is used to preprocess the ultrasonic simulation signals while drilling,and we design a backpropagation neural network model to fit the relationship between the waveform data and time and amplitude.An ultrasonic imaging logging model is established,and the finite element simulation software is used for forward modeling.The response under diff erent measurement conditions is simulated by changing the model parameters,which are used as the input layer of the neural network model;The ultrasonic echo signal is considered as a low-frequency signal modulated by a high-frequency carrier signal,and a low-pass fi lter is designed to remove the high-frequency signal and obtain the low-frequency envelope signal.Then the amplitude of the envelope signal and its corresponding time are extracted as an output layer of the neural network model.By comparing the application eff ects of the various training methods,we fi nd that the conjugate gradient descent method is the most suitable method for solving the neural network model.The performance of the neural network model is tested using 11 groups of simulation test data,which verify the eff ectiveness of the model and lay the foundation for further practical application.

Analysis and optimization of variable depth increments in sheet metal incremental forming

A method utilizing variable depth increments during incremental forming was proposed and then optimized based on numerical simulation and intelligent algorithm.Initially,a finite element method(FEM) model was set up and then experimentally verified.And the relation between depth increment and the minimum thickness tmin as well as its location was analyzed through the FEM model.Afterwards,the variation of depth increments was defined.The designed part was divided into three areas according to the main deformation mechanism,with Di(i=1,2) representing the two dividing locations.And three different values of depth increment,Δzi(i=1,2,3) were utilized for the three areas,respectively.Additionally,an orthogonal test was established to research the relation between the five process parameters(D and Δz) and tmin as well as its location.The result shows that Δz2 has the most significant influence on the thickness distribution for the corresponding area is the largest one.Finally,a single evaluating indicator,taking into account of both tmin and its location,was formatted with a linear weighted model.And the process parameters were optimized through a genetic algorithm integrated with an artificial neural network based on the evaluating index.The result shows that the proposed algorithm is satisfactory for the optimization of variable depth increment.

Neural network method for solving elastoplastic finite element problems

A basic optimization principle of Artificial Neural Network—the Lagrange Programming Neural Network (LPNN) model for solving elastoplastic finite element problems is presented. The nonlinear problems of mechanics are represented as a neural network based optimization problem by adopting the nonlinear function as nerve cell transfer function. Finally, two simple elastoplastic problems are numerically simulated. LPNN optimization results for elastoplastic problem are found to be comparable to traditional Hopfield neural network optimization model.

Optimization of press bend forming path of aircraft integral panel

In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.

Development of a Methodology for Determination and Analysis of Thermal Displacements of Machine Tools Using Finite Elements Method and Artificial Neural Network

In the processes of manufacturing, MT （machine tools） plays an important role in the manufacture of work pieces with complex and high dimensional and geometric accuracy. Much of the errors of a machine tool are those which are thermally induced which are from internal and external heat sources acting on the machine. In this paper, a methodology for determining and analyzing the thermal deformation of machine tools using FEM （finite element method） and ANN （artificial neural networks） is presented. After modeling the machine using FEM is defined the location of the heat sources, it is possible to obtain the temperature gradient and the corresponding thermal deformation at predetermined periods. Results obtained with simulations using the software NX.7.5 showed that this methodology is an effective tool in determining the thermal deformation of the machine, correlating the temperature reading at strategic points with volumetric deformation at the tool tip. Therefore, the thermal analysis of the errors in the pair tool part can be established. After training and validation process, the network will be able to make the prediction of thermal errors just stating the temperature values of specific points of each heat source, providing a way for compensation of thermally induced errors.

Combined back-analysis method of ground stress based on refined geological modeling

A new back-analysis method of ground stress is proposed with comprehensive consideration of influence of topography, geology and nonlinear physical mechanical properties of rock on ground stress. This method based on non-uniform rational B-spline (NURBS) technology provides the means to build a refined three-dimensional finite element model with more accurate meshing under complex terrain and geological conditions. Meanwhile, this method is a back-analysis of ground stress with combination of multivariable linear regression model and neural network (ANN) model. Firstly, the regression model is used to fit approximately boundary loads. Regarding the regressed loads as mean value, some sets of boundary loads with the same interval are constructed according to the principle of orthogonal design, to calculate the corresponding ground stress at the observation positions using finite element method. The results (boundary loads and the corresponding ground stress) are added to the samples for ANN training. And on this basis, an ANN model is established to implement higher precise back-analysis of initial ground stress. A practical application case shows that the relative error between the inversed ground stress and observed value is mostly less than 10 %, which can meet the need of engineering design and construction requirements.