基于神经网络模型的P-GMAW焊缝成形过程仿真

脉冲熔化极气体保护焊(P-GMAW)是一种高效、适应性强的焊接方法之一,在工业生产中得到了广泛的应用.文中以低碳钢P-GMAW为对象,研究其焊缝成形过程的建模与仿真方法.文中首先利用BP神经网络,建立了该过程的动态模型,然后利用该模型的稳态与动态仿真揭示了P-GMAW过程的成形规律.同时,文中提出了一种利用神经网络模型...     

Weld pool shape prediction in plasma augmented laser welded steel

Abstract

A welding process that combined plasma arc welding with laser welding was used to make autogenous bead on plate welds on a sheet stock of a carbon steel. A wide range of welding parameters (arc current, laser power, weld speed) was employed. The experimental weld pool shapes were analysed and the data were used to train a neural network to predict weld pool shape as a function of process conditions. The predictions of the neural network model showed excellent agreement with the experimental results, indicating that a neural network model is a viable means for predicting weld pool shape. Using the model, a parametric study was carried out to examine the influence of process conditions on the final weld pool profile.     

焊接柔性加工单元中熔池的实时控制

机器人焊接过程中熔池实时控制系统是焊接柔性加工单元 (WFMC)中保证良好焊接质量的一个重要子系统。文中建立了WFMC中焊接质量实时控制子系统并实现了该子系统与WFMC中央监控计算机的实时可靠通讯。在获得了焊接熔池特征参数的基础上 ,建立了焊接过程熔池正面参数和焊缝背面参数的神经网络模型。模型根据熔池正面参数可实时预测焊缝背面宽度。并设计了神经元自学习比例求和微分(PSD)控制器 ,通过调整脉冲峰值电流 ,实现了机器人脉冲钨极气体保护焊 (GTAW )过程中通过正面熔池传感对焊接焊缝背面宽度的实时控制。控制试验证明控制器可有效地对焊接过程进行控制     

基于组合模型的MAG焊工艺参数多目标优化

以MAG焊焊接电压、焊接速度、送丝速度为可调工艺参数，开展了三因素三水平全因子平板对接焊和堆焊试验.基于试验数据建立了误差反向传播神经网络、径向基神经网络和克里金模型来预测焊缝余高、接头抗拉强度和冲击吸收能量.模型预测结果显示，所建立模型均能较好的预测焊缝性能，但是没有一个模型能同时最佳预测三种焊缝性能且各模型预测波动较大. 为了进一步提升预测精度和稳定性，将误差反向传播神经网络、径向基神经网络和克里金模型以线性加权法组合. 结果表明，组合模型能提升预测的精度和稳定性.基于组合模型，采用NSGA-II算法实现多目标优化，得到并验证了焊缝余高、接头冲击吸收能量和抗拉强度三者间的非劣解.验证结果表明焊接工艺多目标优化对实现焊缝综合性能整体最优以及焊接精细化应用具有较大的指导意义.     

基于熔池视觉特征的铝合金双丝焊熔透识别

熔透是焊接质量的重要评价指标之一,铝合金对焊接工艺敏感性较高,容易出现熔透不均匀情况.试验利用近红外视觉传感方法获取了铝合金单面焊双面成形焊接过程中未熔透、熔透和过熔透三种情况下的清晰熔池图像,通过图像处理获得了准确的熔池轮廓,定义并提取了熔宽、半长、面积、周长和抛物线系数等能反映熔透状态的熔池特征参数,建立了基于BP神经网络的铝合金双丝焊熔透识别模型.结果表明,5-13-3结构的BP神经网络对熔透状态识别的正确率最高,达到89.05%.     

Neural network model of heat and fluid flow in gas metal arc fillet welding based on genetic algorithm and conjugate gradient optimisation

Abstract

Although numerical calculations of heat transfer and fluid flow can provide detailed insights into welding processes and welded materials, these calculations are complex and unsuitable in situations where rapid calculations are needed. A recourse is to train and validate a neural network, using results from a well tested heat and fluid flow model to significantly expedite calculations and ensure that the computed results conform to the basic laws of conservation of mass, momentum and energy. Seven feedforward neural networks were developed for gas metal arc (GMA) fillet welding, one each for predicting penetration, leg length, throat, weld pool length, cooling time between 800°C and 500°C, maximum velocity and peak temperature in the weld pool. Each model considered 22 inputs that included all the welding variables, such as current, voltage, welding speed, wire radius, wire feed rate, arc efficiency, arc radius, power distribution, and material properties such as thermal conductivity, specific heat and temperature coefficient of surface tension. The weights in the neural network models were calculated using the conjugate gradient (CG) method and by a hybrid optimisation scheme involving the CG method and a genetic algorithm (GA). The neural network produced by the hybrid optimisation model produced better results than the networks based on the CG method with various sets of randomised initial weights. The CG method alone was unable to find the best optimal weights for achieving low errors. The hybrid optimisation scheme helped in finding optimal weights through a global search, as evidenced by good agreement between all the outputs from the neural networks and the corresponding results from the heat and fluid flow model.     

A Neural Network for Weld Penetration Control in Gas Tungsten Arc Welding

Realizing of weld penetration control in gas tungsten arc welding requires establishment of a model describing the relationship between the front-side geometrical parameters of weld pool and the back-side weld width with sufficient accuracy. A neural network model is developed to attain this aim. Welding experiments are conducted to obtain the training data set （including 973 groups of geometrical parameters of the weld pool and back-side weld width） and the verifying data set （108 groups）. Two data sets are used for training and verifying the neural network, respectively. The testing results show that the model has sufficient accuracy and can meet the requirements of weld penetration control.     

Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals

This paper addresses the weld joint strength monitoring in pulsed metal inert gas welding (PMIGW) process. Response surface methodology is applied to perform welding experiments. A multilayer neural network model has been developed to predict the ultimate tensile stress (UTS) of welded plates. Six process parameters, namely pulse voltage, back-ground voltage, pulse duration, pulse frequency, wire feed rate and the welding speed, and the two measurements, namely root mean square (RMS) values of welding current and voltage, are used as input variables of the model and the UTS of the welded plate is considered as the output variable. Furthermore, output obtained through multiple regression analysis is used to compare with the developed artificial neural network (ANN) model output. It was found that the welding strength predicted by the developed ANN model is better than that based on multiple regression analysis.     

电弧焊熔透ICA-BP神经网络识别模型

以氩弧焊熔透状态识别为研究对象,研究一种基于ICA (Imperialist Competitive Algorithm) 的BP(Back Propagation)神经网络识别模型方法. 首先利用ICA全局搜索不易陷入局部极值及搜索速度快的特点对神经网络权值和阈值初始化,再用BP算法对神经网络进行训练. 通过摄取焊接过程中的熔池图像,提取熔池面积、熔宽以及熔池质心位置作为神经网络预测模型的输入量,分析熔池图像三个特征与焊缝熔透状态的映射关系,最终建立熔透状态预测模型. 结果表明,采用ICA-BP神经网络能够有效地预测焊缝的熔透状态.     

铝合金激光-多股绞合焊丝MIG复合焊特性分析

选用多股绞合焊丝替代传统焊丝,将激光热源与多股绞合焊丝MIG焊热源相匹配.借助高速摄像系统,提取焊接过程中熔池和匙孔特征量,开展5A06铝合金激光-多股绞合焊丝MIG复合焊工艺特性研究,探讨了不同工艺参数下焊缝成形与熔池行为相关性及焊接气孔规律性研究.结果表明,主要焊接工艺参数对焊缝成形的影响规律与常规焊丝激光-电弧复...     

聚变堆用奥氏体不锈钢不同激光输出模式熔丝特征

针对聚变堆用316LN奥氏体不锈钢材料,分别在连续激光和脉冲激光模式下进行了激光填丝焊接试验。主要研究了不同焊接工艺参数下的焊丝熔入特征及其对焊缝质量的影响,并对连续激光与脉冲激光焊接熔池流动及熔池形貌、接头的显微组织进行了研究。结果表明,连续激光填丝焊和脉冲激光填丝焊在合适的焊接工艺参数下均能获得焊丝液桥过渡,且熔池铺展均匀、焊缝成形良好。与脉冲激光填丝焊相比,连续激光填丝焊在坡口中的熔池长度约是脉冲激光填丝焊的3倍,温度梯度较大,更易产生侧壁未熔合和贯穿焊缝中心的凝固裂纹等缺陷。连续激光填丝焊的焊缝显微组织以柱状晶为主,由焊缝两边向焊缝中心生长,方向性强。脉冲激光填丝焊的焊缝两侧显微组织以柱状晶为主,各个区域都受到了相邻脉冲的重复作用,具有周期性,产生二次结晶,有助于熔池的搅动和晶粒细化,打乱了枝晶生长的方向性;焊缝中心区域为方向各异的柱状晶和等轴晶,枝晶间距减小,能够抑制裂纹的产生。 创新点： 首次提出通过脉冲激光调控结晶形态以抑制厚板焊接中裂纹的焊接工艺,可在不添加任何外部设备的基础上实现对裂纹的有效控制,打破了传统焊接工艺带来的局限性。     

Modeling, optimization and classification of weld quality in tungsten inert gas welding

In this paper, a neural network is used to construct the relationships between welding process parameters and weld pool geometry in tungsten inert gas (TIG) welding. An optimization algorithm called simulated annealing (SA) is then applied to the network for searching the process parameters with an optimal weld pool geometry. Finally, the quality of aluminum welds based on the weld pool geometry is classified and verified by a fuzzy clustering technique. Experimental results are presented to explain the proposed approach.     

Prediction of bead geometry in pulsed GMA welding using back propagation neural network

This paper presents the development of a back propagation neural network model for the prediction of weld bead geometry in pulsed gas metal arc welding process. The model is based on experimental data. The thickness of the plate, pulse frequency, wire feed rate, wire feed rate/travel speed ratio, and peak current have been considered as the input parameters and the bead penetration depth and the convexity index of the bead as output parameters to develop the model. The developed model is then compared with experimental results and it is found that the results obtained from neural network model are accurate in predicting the weld bead geometry.     

2060铝锂合金扫描填丝焊接工艺

针对铝锂合金焊后易产生气孔、抗拉强度低的缺点,提出“∞”形激光扫描填丝焊接工艺方法,以2 mm厚2060铝锂合金为研究对象开展对接焊接试验研究,探究激光扫描填丝焊接方法对铝锂合金焊接缺陷抑制作用. 借助高速相机摄像系统,探究了激光扫描填丝焊接工艺下熔池的动态演变过程,同时探究了扫描参数对焊缝气孔的影响规律及扫描填丝工艺对气孔的抑制机理. 采用曲面响应统计方法探究工艺参数对抗拉强度的影响,并给出工艺参数组合与抗拉强度的定量关系及最优参数组合,焊接接头最大抗拉强度可达382 MPa,为母材的76.4%. 结果表明,“∞”形激光扫描填丝焊接工艺下熔池流动平稳,小孔喷发强度较弱且呈现出周期性;“∞”形激光扫描填丝焊接工艺可以有效抑制焊缝气孔,提高铝锂合金焊接质量.     

焊缝偏差RBF神经网络预测算法

以10 kW大功率光纤激光焊接304奥氏体不锈钢板为试验对象,研究一种焊缝偏差预测算法.利用红外摄像机摄取焊接过程中的熔池红外图像,提取匙孔质心、匙孔形状参数和热堆积效应参数等反映激光束与焊缝位置偏差的特征量作为径向基函数RBF神经网络预测模型的输入量,建立焊缝偏差RBF神经网络预测模型.选择焊缝偏差特征量作为训练样本并对预测模型进行训练,建立焊缝偏差预测模型.结果表明,该模型能够对大功率光纤激光焊接过程中的激光束与焊缝位置之间的偏差进行有效预测.     

Neural network modeling for weld shape process of P-GMAW

Weld shape control is a fundamental issue in automatic welding. In this paper, a double side visual system is established for pulsed gas metal arc welding （ P-GMAW） , and both topside and backside weld pool images can be captured and stored continuously in real time. By analyzing the weld shape regulation with the molten metal volume, some topside weld pool characterized parameters （WPCPs） are proposed for determining penetration in butt welding of thin mild steel. Moreover, some BP network models are established to predict backside Weld pool width with welding parameters and WPCPs as inputs.     

APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELING TO PLASMA ARC WELDING OF ALUMINUM ALLOYS

1.Weldingisacomplicatedprocesswhichisnoallnear,variable--coupledandrandom,soitissodifficulttofindaaccuratelawthatwehavetodependonscientificessaily.Ihrecentyears,autOInationOfarcweldingProcess,whichisbasedoninduStrialrobots,hasbeentakenPublicopinionseriously,andthebasisofconedofweldingPIDCessistoestablishinathernaticalmodelOfweldingprocessandweldingqUality'AtPreSent,weOftenuseformulaOfrecessionexperiencetosatisfytheneedOfweldingtechaology.aamrecessionmodelcanbeusedtOfindstatisticallawout…     

高速CMT焊送丝速度和焊接电流波形参数的优化

在5 m/min的平均送丝速度、1.8 m/min的焊接速度下开展高速CMT焊正交试验,研究焊接工艺参数对焊接过程及焊缝成形的影响,分析高速下焊接过程不稳定的原因. 结果表明,峰值送丝速度、峰值持续时间tp和峰值电流Ip的影响作用依次降低. 焊接工艺参数匹配不合适时(包括焊机的专家参数),高速焊时频繁发生周期性“粘丝”现象,焊缝成形不均匀、咬边严重. 峰值送丝速度过快、高速下的熔滴后拖和熔池前部金属薄层温度过低是“粘丝”产生的主要原因. 通过合理匹配上述三个参数消除了粘丝并获得良好的焊缝成形.     

GTAW神经网络-模糊控制技术的研究

研究神经网络与模糊控制融合技术,构成钨极气体保护电弧焊ＧＴＡＷ神经网络＝模糊控制系统。重点论述神经网络和模糊逻辑在熔深建模和控制以地缝跟踪方面的应用。通过视觉传感ＣＣＤ获取电弧区图像和熔池表面的应用。种描述深的神经焊缝间隙是量来精确估算熔的精度,同时结合模糊逻辑提高熔深的控制精度。针对弧焊过程非线性以焊炬伺服系统动态过程难以用角的数学模型来表达的问题,设计焊缝跟踪自调整模糊控制器,通过自适应共振理     

基于Elman网络算法的Inconel625合金堆焊稀释率的控制

文中建立了5-8-3结构的反馈Elman神经网络模型,以电弧长度、焊接电流、焊接速度、送丝速度和保护气流量为输入量,堆焊焊缝的熔宽、熔高和稀释率为输出量进行堆焊仿真计算分析.计算结果表明,Elman模型的预测结果比BP和GRNN神经网络更精确.建立了以电弧长度（X）、堆焊电流（Y）和送丝速度（Z）为空间坐标,堆焊稀释率δ等于f（X,Y,Z）为目标函数的四维图像来确定δ≤5%的堆焊工艺窗口.分别进行Elman模型仿真计算和堆焊工艺试验,得到的稀释率δ分别为2.55%和3.32%,仿真计算的稀释率的相对误差约0.8%,证实了Elman模型预测的Inconel625合金堆焊工艺窗口的可行性与可靠性.