基于RBF网络的P-GMAW焊缝成形过程建模

分析了薄板平板堆焊熔透的影响因素,利用径向基函数网络（RBFN）,建立了脉冲熔化极气体保护焊（P-GMAW）的焊缝成形过程模型.结果表明,在P-GMAW薄板堆焊中,以焊接规范为输入变量,利用该网络模型能很好的预测背面熔宽.     

基于模糊小脑模型神经网络的直线伺服跟踪控制研究

在高精度直线伺服跟踪控制系统中,为使输出响应快速地跟踪输入指令,需要克服系统滞后、未建模动态、不确定性以及负载变化的影响,文中提出了一种模糊小脑模型神经网络(FCMAC)直接逆控制的方案,可动态地克服这些影响.文中给出了较详细的原理分析及实现过程.仿真结果表明,该方法能够明显提高直线伺服系统的快速跟踪能力,并使系统具有较强的适应性和鲁棒性.     

应用神经网络优化压边力

以汽车行李箱盖冲压成形过程为例,提出一种利用人工神经网络技术对压边力控制曲线进行优化的方法,将压边力优化理论与数值仿真技术相结合,建立了压边力优化RBF神经网络模型。仿真结果证明,采用优化后的变压边力控制曲线能有效改善板料的成形性能和成形质量。     

波形环橡皮成形不均匀变形仿真分析

采用仿真与实验相结合的方法,对超薄壁波形环橡皮成形不均匀变形进行分析.解决了该成形过程中有限元建模关键技术,如几何模型的建立、 材料模型的建立以及加载边界条件处理等,建立了符合实际又兼顾计算精度的超薄壁波形环橡皮成形有限元仿真模型.基于所建立的仿真模型,分析了该成形过程中的不均匀变形特征.结果表明:随橡皮成形过程的进行...     

遗传算法结合神经网络实现拉延筋优化设计

传统的冲压模具设计中,拉延筋设计和布置主要依靠经验,这使得模具设计和制造周期延长.以某汽车侧壁外板的拉深工序为例,讨论了神经网络技术与遗传算法在拉延筋优化设计中的综合应用问题.建立了反映板料成形参数与拉延筋阻力之间非线性映射关系的BP网络模型.利用该训练好的神经网络可以实现拉延筋的优化设计.由于相对于进行工艺试验来说数值仿真比较省时省力,因此,利用Dynaform模拟汽车侧壁外板的拉深成形过程,建立训练样本.在网络的训练方法上利用遗传算法进行了优化,有效地提高了神经网络的模拟精度.     

基于改进神经网络的不确定性X-Y定位平台自适应控制

描述了X-Y平台实验系统的组成及其模型的建立过程.针对X-Y定位平台中存在的载荷以及摩擦参数的不确定性问题,提出了一种基于改进神经网络来补偿X-Y定位平台不确定性的方法.采用自适应遗传算法调整神经网络的权值系数,增强了神经网络的学习能力,使该定位平台的控制精度、鲁棒性和动态特性得到了改善.计算机仿真结果表明这种控制设计方案具有很好的动态特性.     

基于小波神经网络和粒子群算法的铝合金板冲压回弹工艺参数优化

针对铝合金复杂件冲压后出现的较大回弹缺陷,同时为减少冲压成形工艺参数的优化时间,使用有限元仿真软件DYNAFORM对冲压成形及回弹过程进行数值模拟,在确保数值模拟与试验结果基本一致的基础上,利用代理模型对回弹进行了优化研究。以NUMISHEET'96 S梁为研究对象,凸模圆角半径、凹模圆角半径、压边力、板料厚度作为影响因素,成形后最大回弹值作为成形目标,运用拉丁超立方抽样,通过数值仿真获得样本数据,建立影响因素与成形目标之间的小波神经网络代理模型,利用粒子群算法对该模型迭代寻优获得最优工艺参数。结果表明:小波神经网络能较好地描述板料工艺参数与回弹之间的映射关系,优化后成形件的回弹量大大减小。     

机械臂自适应神经网络控制虚拟实现

针对机械臂自适应神经网络控制效果不能直观显示的问题,在深入研究各种虚拟仿真,可视化仿真方法的基础上提出了一种基于ADAMS和MATLAB的虚拟样机联合仿真方法.采用该方法对三自由度机械臂进行动态仿真,首先在ADAMS中建立机械臂模型,然后在MATLAB中实现自适应神经网络控制器和机械臂终端轨迹的设计,最后进行联合仿真.仿真结果非常直观地显示了机械臂的动态控制过程和控制器各参数的实时变化情况,验证了该方法的有效性,为机械臂的复杂控制算法的仿真研究提供了新的有效方法.     

基于动态递归模糊神经网络的轧机辊缝动态辨识

模糊系统和神经网络,由于具有逼近任意连续非线性映射的特性,而广泛应用于系统的辨识与控制。但是传统的模糊神经网络是一种静态映射,不适用于动态系统的辨识,而轧制过程中影响轧机辊缝的因素复杂,外界干扰严重,过程参数难以确定,为提高轧机辊缝动态的辨识精度,提出了一种基于动态递归模糊神经网络的辨识模型。轧制仿真结果表明,该模型具有很高的辨识精度。     

基于神经网络和遗传算法的冲压成形多目标优化技术

在冲压成形质量控制中,目标质量间常常是相互冲突的(如破裂和起皱)。传统求解多目标优化问题取决于设计人员对优化模型的理解程度、实践经验等,求解的结果在工程中并非为最合理。文章提出一种集数字化分析技术、神经网络和遗传算法于一体的冲压成形多目标优化设计技术。其以数字化分析的大量结果作为神经网络的学习样本,遗传算法所需的目标函数值由神经网络模型预测,该技术实现了多目标优化过程中遗传算法个体适应度值的动态求解,从而解决了数字化分析计算量大的缺陷。实例验证了该优化技术的有效性,为冲压成形优化设计提供了一种新的方法。     

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.     

Neural network modeling for dynamic pulsed GTAW process with wire filler based on MATLAB

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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.     

Weld pool boundary and weld bead shape reconstruction based on passive vision in P-GMAW

A passive visual sensing system is established in this research, and clear weld pool images in pulsed gas metal arc welding （ P-GMA W） can be captured with this system. The three-dimensional weld pool geometry, especially the weld height, is not only a crucial factor in determining workpiece mechanical properties, but also an important parameter for reflecting the penetration. A new three-dimensional （3D） model is established to describe the weld pool geometry in P-GMAW. Then, a series of algorithms are developed to extract the model geometrical parameters from the weld pool images. Furthermore, the method to reconstruct the 3 D shape of weld pool boundary and weld bead from the two-dimensional images is investigated.     

填丝脉冲GTAW熔池形状定义和图像处理

在填丝脉冲GTAW过程中,从熔池图像上可以观察到熔池形状的变化,如度、长度、后拖角和表面高度等。熟练焊工可以根据变化的情况实时地调整焊接规范,保证焊缝成形稳定,利用获得的焊接熔池图像,采用一定的熔池图像处理算法可以计算出熔池形状的特征参数,以满足过程建模和实时控制的要求。本针对填丝脉冲GTAW熔池形状变化的特点,提出了统一的熔池形状非线性拟合公式和熔池形状参数。设计了快速图像处理算法,满足在实际焊接过程中提取熔池形状参数的要求,试验结果证明,熔池形状非线性拟合公式准确,图像处理算法有效,为进一步焊接过程的建模和控制奠定基础。     

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.     

Neural network modeling and intelligent control of FCAW penetration

The neural network modeling of FCAW penetration is researched in this paper, molten pool image is acquired by CCD, and preweld gap is gotten from laser vision system, the weld penetration is estimated according to the information include welding current, welding voltage, weld width, molten pool half length and gap width. The training samples of network can be partially gotten by numerical simulation. Single neuron self-tuning PID weld penetration controller is designed, and improved Hebb learning algorithm is applied for weights adjusting. Welding current is adjusted to make the weld penetration stable. The results of experiment with various cross-section and preweld gap workpiece show that this system is suitable to molten pool control.     

A method of tuning PID parameters for P-GMAW based on physical experiments

To improve welding quality,a method of proportional-integral-differential (PID)parameters tuning based on pulsed gas metal arc welding (P-GMAW)control was put forward.Aiming at the request of dynamic responsiveness of PGMA W constant current control,a self-developed welding waveform wavelet analyzer was employed.By tuning the proportional parameter,integration time and differential time in sequence,the optimal PID parameters could be achieved.The results showed that,due to the PID parameters tuned by this method,the welding process was stable and the weld bead appearance was nice.The requirement of dynamic responsiveness of P-GMAW constant current control was fully met.     

Pulsed gas metal arc welding of Al–Cu–Li alloy

Abstract

An experimental Al–Cu–Li–Mg–Ag–Zr type alloy in the form of 13.7 mm thick plates was studied for its fusion characteristics using gas metal arc welding (GMAW) and pulsed gas metal arc welding (P-GMAW). High copper 2319 filler of 1.6 mm diameter was used. The burn-off characteristics of 2319 filler wire in GMAW and P-GMAW were experimentally determined, including the relation between pulse current and pulse duration for the desired one-drop detachment per pulse (ODPP) condition and feasible range of pulse parameters. The effect of welding parameters on bead geometry and shape relationships was investigated through beadon-plate experiments in the welding current range above the spray transition current. Reasonably good weld beads were obtained in P-GMAW at currents as low as 194 A and welding speeds of 45 cm min^–1. P-GMAW yielded significantly higher weld penetration compared to GMAW.     

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