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 springback can be acquired using the FEM-PSONN model.     

人工神经网络技术及其在板料成形智能化中的应用

本文在概述人工神经网络特性、BP网络模型及BP算法的基础上 ,对BP算法改进、训练样本及网络结构等相关技术进行了总结。重点综述了人工神经网络技术在板料成形专家系统、成形力预测、参数识别、智能控制、故障诊断、缺陷分析、板料成形性能研究和模具优化设计等板料成形智能化相关技术中的应用 ,探讨了应用中存在的问题 ,并展望其发展趋势     

在数值模拟基础上利用神经网络进行缺陷预测

利用体积可压缩的刚塑性有限元法,对多孔体材料镦粗成形过程进行了数值模拟计算,并结合Ｌｅｅ－Ｋｕｈｎ提出的极限压缩应变准则得到了坯料鼓形表面出现裂纹的一系列极限参数。     

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.Furthe...     

脉冲PAW快速成形焊缝尺寸的预测模型

介绍了一种基于脉冲等离子焊接快速成形的方法,并采用Taguchi法对单道成形试验进行了合理设计,从而获得了多组焊接工艺参数下的熔宽和余高数据.通过遗传算法（GA）结合BP神经网络的方法建立了等离子焊接快速成形的预测模型,该模型预测了不同焊接成形工艺参数下单道成形的熔宽和余高.结果表明,通过误差分析和线性回归的方法验证了模型具有较高的预测精度和泛化能力,能够合理的预测焊道尺寸,并能推广到多层多道堆积成形尺寸的预测.     

基于RES理论的岩体失稳模式判别及其智能实现

基于完全耦合模型解决复杂岩石力学问题的方法论——岩石工程系统理论(RES)的基本原理，构造了地下工程岩体失稳模式判别的智能预测模型，并采用具有自学习、非线性映射和数据挖掘功能的模糊自组织人工神经网络模型ANN来实现RES交互作用矩阵的编码。通过一个实例说明这种方法具有良好的实用性。     

铝合金铸造凝固过程有限元模拟的神经网络优化

应用神经网络优化了铝合金铸造凝固有限元模拟(FEM)过程,使得CPU时间最小化.在优化中,通过粗化FEM网格减少CPU时间.模拟精度降低的代价通过神经网络拟合机制来补偿.拟合机制由自适应学习率-动量因子的误差反向传播改进算法来实现.FEM模拟的控制方程是基于焓变的Fourier导热方程.模拟结果为开模循环周期20 s的温度场,几何中心与浇道等铸件特定部位的温度-时间曲线.本工作证实了对凝固FEM技术实施神经网络优化的可行性.     

提高开式冷挤压极限变形程度预测精度方法的研究

充分利用BP-GA网络相结合方法和BP网络较强的函数逼近能力,以及GA算法的高效率全局搜索能力,将遗传算法和BP网络相结合并优化网络权值．建立一种新的极限变形程度预测模型,与基于BP算法的预测模型相比较,具有迭代次数少、训练时间短和预测精度比较高的优点,其计算分析结果也可直接用于实际生产。     

Application of neural network and FEM for metal forming processes

This paper proposes a new technique to apply the artificial neural network in metal forming processes. A three-layer neural network is used and a back propagation algorithm is employed to train the network. It is determined by applying the ability of function approximation of the neural network to the initial billets which satisfy the minimum of incomplete filling in the die cavity. The die geometry for cylindrical pulley is designed to satisfy the design conditions of the final product. The proposed schemes have been successfully adapted to find the initial billet size for axisymmetric rib-web product and to design the die geometry for cylindrical pulley. The neural network may reduce the number of finite element simulation for designing the die of forging products and further it is usefully applied to multi-stage process planning.     

Recognition of chatter with neural networks

Chatter deteriorates surface finish, reduces tool life, and damages machine tools. A chatter development prediction procedure is proposed for the cylindrical turning of long slender bars. The procedure uses two synthetically trained neural networks to recognize the harmonic acceleration signals and their frequency, and based on these observations, the future vibration characteristics of the system are estimated. The developed neural networks are capable of identifying 98% of the harmonic signals with over 90% certainty and estimate their frequencies with less than ±5% error from very short data sequences (only 11 sampled points). The accuracy of the neural networks is equivalent to time domain time series method based approaches; however, the proposed procedure can be implemented very quickly by using commercially available neural network hardware and software, and can use the new neural network chips to make the estimations very quickly by using parallel processors. The validity of the chatter prediction procedure is also demonstrated on the experimental data.     

基于神经网络的并联机床表面粗糙度预测

将人工神经网络改进后应用到并联机床粗糙度的预测模型中,有效预测了机床进给速度、主轴转速、加工角度、加工作用力以及加工次数等工艺参数变化下对粗糙度的影响。结果表明:当网络的训练步数控制在200到400次时,整个网络模型的训练样本均方误差是平稳且收敛的,并且训练中加入的检验样本的预测误差可以控制在5%以下,满足预测模型的训练要求,证明经过改进的神经网络预测模型用于实际加工预测过程中是可行的,且精度较高。     

基于代理模型的板料成形优化技术进展

综述了板料成形优化技术现状,针对优化中计算成本高的问题,提出了基于代理模型的优化技术,包括可立格模型、响应面模型、神经网络模型等。从实验设计、代理模型的建立,以及优化求解等方面进行了阐述。针对产品设计中因素的随机性和不确定性,为减小目标函数对设计变量的敏感性,将稳健设计理论应用于板料成形优化中,以提高产品的可靠性和稳健性。     

TC4钛合金神经网络本构模型及在有限元模拟中应用

利用Zwick/Roell Z100材料试验机,对TC4钛合金进行等温恒应变速率下的单向拉伸试验。基于获得的试验数据,采用BP神经网络技术建立了该合金的高温本构关系模型,并对其预测性能进行分析。基于ABAQUS/Explcit平台进行材料子程序二次开发,将神经网络本构模型嵌入到有限元计算中,实现了TC4钛合金高温变形的数值模拟。结果表明,神经网络本构模型预测精度很高,可以准确地描述TC4钛合金在热态下的动态力学性能。神经网络本构模型应用于有限元模拟可行且有效。     

Modelling and prediction of cut shape for plasma arc cutting based on artificial neural network

Abstract

An artificial neural network approach for the modelling of plasma arc cutting processes is introduced. Neural network models have been proposed for predicting the cut shape and estimating the special cutting variables. The implementation of artificial neural networks in the modelling of cutting processes is discussed in detail. The performance of the neural networks in modelling is presented and evaluated using actual cutting data. Moreover, prediction applications of the above neural network models are described for various cutting conditions. It is shown that estimated results based on the proposed models agree well with experimental data; the neural network models yield good prediction results over the entire range of cutting process parameters spanned by the training data. The testing and prediction results show the effectiveness and satisfactory prediction accuracy of the artificial neural network modelling. The developed models are applicable to carbon steel.     

基于神经网络的药芯焊丝GMAW发尘率预测

焊接工艺对焊接发尘率有直接的影响,建立基于相关焊接工艺参数的焊接发尘率预测模型,预测特定焊接工艺的发尘率对控制和降低焊接烟尘的排放具有重要意义。鉴于焊接发尘率影响因素复杂,存在高度非线性特征,提出了基于神经网络的熔化极气体保护焊(GMAW)焊接发尘率的预测模型。通过药芯焊丝E501T-1发尘率实测数据,分别建立了BP和Elman神经网络模型,并采用遗传算法(GA)对2种神经网络进行了优化。基于15组实测数据的验证,结果表明,采用遗传算法优化后,BP和Elman神经网络模型的预测合格率分别提升了6.7%和13.4%,遗传算法优化的BP神经网络模型(GA-BP)的均方误差为586.21,平均绝对百分比误差为3.01%,均为4个模型中最小,其预测结果更为准确可靠。基于GA-BP模型所预测数据,对不同焊接电流和电弧电压的发尘率进行预测,在一定的焊接速度和保护气流量条件下,焊接电流约为170 A,电弧电压约为26 V时,焊接发尘率最小。 创新点： (1)将神经网络模型引入到焊接发尘率数值预测中,并通过遗传算法对神经网络的权值和阈值进行优化,提高了预测准确性和可靠性。 (2)根据优化后的模型的预测结果,分析了焊接电流和电弧电压对发尘率的影响规律,为进一步控制焊接发尘率提供了有益的指导。     

无网格法在金属塑性成形中的研究与应用进展

介绍了无网格方法的基本原理;论述了无网格法在金属塑性成形领域里国内外的研究与应用现状;总结了相关关键处理技术,包括权函数的选取、本质边界的施加、离散积分方案、动态接触边界的自动处理以及金属塑性力学摩擦模型的选取等;最后对无网格法在金属塑性成形领域未来的研究方向进行了展望,并指出无网格法在金属塑性成形领域将有着更为广阔的发展与应用前景。     

基于GA-神经网络的工程陶瓷材料电加工参数优化研究

运用GA-高阶模糊BP神经网络对电火花线切割过程中的主要电参数（脉冲电流、脉冲宽度、工作电压、脉冲间隔、功率管数）和相应的输出参数进行学习、训练和优化，让神经网络具有预测的能力。利用优化的电参数对工程陶瓷材料Al2O3-TiC（含量30％）进行电火花线切割实验研究，得到了与GA-神经网络输出所一致的结果。Al2O3-TiC加工表面获得了最小的表面残余应力和最佳的耐磨强度。     

Spray Forming Quality Predictions via Neural Networks

]To produce consistently high-quality spray-formed parts,correlations must be made between the input process parameters and the final part quality. The Spray Forming Technology Group at the Naval Surface Warfare Center decided to “model” this correlation through the use of artificial neural networks. In this study, neural networks accurately predicted trends in spray forming process outputs based on variations in process inputs. The graphs generated by the neural network prediction help to define the optimal operating region for the spray forming process and indicate the effect of changing input process parameters on final part quality The Johns Hopkins University Department of Materials Science and Engineering, Baltimore,MD 21218 United States Naval Academy, Department of Mechanical Engineering, Annapolis, MD 21401.     

漏磁检测信号的反演

针对漏磁检测中缺陷特征参数的评估问题，分析了依据漏磁测量结果反演缺陷参数的研究方法及步骤，并分别应用基于有限元模型和人工神经网络的方法实现漏磁检测缺陷参数的反演，取得了较好的反演效果。     

STUDY ON CONSTITUTIVE RELATIONSHIP OF Ti-5Al-2Sn-2Zr-4Cr-4Mo ALLOY AND SIMULATION OF HOT FORMING PROCESSES

.~theSofar,finiteelementmethodhaswidelybeenusedinmetalfoeingprocesses,whichcanhelptoshortenthedevelopmentcycleandreducetheproductcosts.ConstitutiverelationshipisabridgebetweenthedefonnationbehaViorofmaterialsandallkindsOfthermomechanicalparameters,anditisalsoapresupPOsitiontothesimulationOfmetaldeformationprocessesbyusingfiniteelementmethod.Fwhermore,itisusuallynon--linearandcomplex,owingtoavallationinstructUredabingplasticdefonnation,Particularlyoccultinginsupendloys.FOrmanyyears,researche…