一种多用途的新焊接工艺--摩擦搅拌焊接法

人们对于弧光闪闪的电弧焊并不陌生,从与生活密切相关的手工作坊,到现代化大型制造、修理工厂的自动生产线,电弧焊随处可见.目前,广泛应用于汽车、火车、飞机、船舶等铝合金的焊接作业,一般采用了MIG(金属极惰性气体保护电弧焊)等.但是,最近以来,一种焊接变形小,焊接质量高于电弧焊,并具有一系列独特优点的摩擦搅拌焊接工艺取得了长足的发展,在铝合金的焊接中显示了明显的优势,可取得良好的经济技术效益.而普通电弧焊必须用高温电弧熔化被接合金属及填充焊丝,所以在任何接合部均发生材料的热变形问题,尤其是车辆的车体、船舶等板材如发生热变形,处理起来很麻烦,效果也不好.     

基于U型网络的K-TIG焊熔池图像分割方法研究

小孔钨极惰性气体保护焊K-TIG(Keyhole Tungsten Inert Gas Welding)的强烈氩弧光会对熔池液面与焊件的成像造成干扰。针对其熔池图像呈现边界模糊、特征尺寸差异大、形态不规则的特点,设计了一种基于注意力机制的多尺度特征融合语义分割模型,对焊接熔池区域图像进行分割。首先,利用并行结构的多尺度非对称瓶颈单元替换UNet在特征提取过程中的卷积块,加强对不同尺度特征提取的能力,强化对熔池轮廓的表征能力;然后,在上采样阶段使用跨层的注意力模块引导网络模型更加关注熔池的锁孔区域;最后,在进行网络训练之前,对训练集的图片采用Multi-Scale Retinex算法执行颜色和形态的图像增强。实验结果显示,该神经网络的分割结果与熔池实际区域在Dice系数、MIOU以及F1系数的指标上分别达到95.78%、83.32%和91.86%。     

卡尔曼滤波焊缝跟踪控制

在视觉传感的电弧自动焊接过程中,需要根据视觉信息来控制电弧准确地跟踪焊缝.由于强烈的弧光干扰,使得从焊接区图像中直接提取电弧与焊缝的偏差信息十分困难.为此提出一种利用熔池图像质心和卡尔曼滤波来间接获取电弧与焊缝偏差的方法.选择熔池图像质心作为状态向量,建立基于图像质心的状态方程和焊缝位置测量方程.利用卡尔曼滤波消除过程噪声和测量噪声的影响,通过对熔池图像质心的状态估计,准确获取焊缝位置以及电弧与焊缝之间的偏差量,为自动焊接过程的焊缝跟踪控制提供准确信息.焊接试验结果表明,利用卡尔曼滤波方法可有效降低过程噪声和测量噪声的影响,从而提高焊缝跟踪控制精度.     

经济型焊缝跟踪视觉传感器的研制

利用普通的焊接护目镜玻璃制作了熔池图像视觉传感器,在304#不锈钢等离子弧焊缝跟踪试验中获得的图像是足够清晰的,能够清楚地看到接缝和焊接熔池.在工厂车间里进行的试验证明:这种低成本的视觉传感器可以用于304#不锈钢的等离子弧焊接过程的实时焊缝跟踪和控制.     

铝合金TIG焊缝背面宽度的智能控制研究

一、引言 大型铝合金结构件的焊接中,钨极氩弧焊接是普遍采用的方法.焊接熔池的大小、形状和动态变化是影响焊缝内在质量及外表成形好坏的主要因素,在焊接生产中,工件形状尺寸、工装散热状态、装配间隙的变化、焊接位置的差异以及焊接参数的波动等状况常常导致焊缝熔透不均匀,影响产品的质量可靠性和稳定性.     

PAW过程弧光传感技术研究

小孔的稳定性是影响穿孔等离子弧焊焊接过程稳定性及接头质量的重要因素 .在等离子弧发射光谱分析的研究基础上 ,研制了轻便实用的弧光传感器 ,采用普通光电器件和滤光镜片替代赘重昂贵的光谱分析系统 ,可以捕捉到表征小孔特征状态的信号     

双丝共熔池GMAW焊接熔池流场和温度场数学模型

基于对焊接熔池内流体的流动和热传递过程的分析,建立描述三维双丝共熔池GMAW焊接熔池流场和温度场的准稳态控制微分方程组,并将控制方程组离散化,为GMAW焊接过程的计算饥模拟提供基础。     

脉冲GTAW焊缝成形智能控制方法

在作者前期工作的基础上,进一步展示了模糊神经网络与专家系统等智能化方法在 电弧焊动态过程与焊缝成形控制中的应用结果.在模糊推理与神经网络结合的单人单出焊接电 流控制器FNNC设计的基础上,设计了专家系统控制器用于调节焊接速度,完成了焊接熔池动 态过程双人双出智能控制器的设计,实现了脉冲GTAW正反面熔宽与焊缝成形的同时智能控 制.与前期工作一并构成了对电弧焊接熔池动态过程和焊接质量智能控制途径的成功探索.     

脉冲GTAW熔池动态过程模糊神经网络建模与控制

展示了模糊推理与神经网络结合在脉冲GTAW熔池动态过程智能控制中的应用研究 结果.建立了脉冲GTAW平板对接动态过程特征:正反面熔池的最大宽度、长度与面积等参数 的神经网络模型,基于实验数据采用模糊辨识方法提取焊接过程的模糊控制规则,进而设计了 具有自学习适应能力的模糊神经网络控制器.建立了脉冲GTAW熔池动态过程智能控制系统, 焊接实验验证了所设计的模糊神经网络控制器具有智能控制效果.     

放弃独显更精彩 戴尔新灵越14z笔记本电脑

戴尔灵越（Inspiron）Z系列是面向普通消费者的轻薄型笔记本电脑．近期它也进行了升级．将更强大的性能集成到了更轻薄的体型中。其壳体材质采用了真正的铝合金金属材质．表面拉丝工艺面板．提供黑色或红色可换外壳。     

Welding torch attitude-based study of human welder interactive behavior with weld pool in GTAW

Skilled human welder adjusts welding parameters including the welding torch attitude, moving speed and position to control weld quality and avoid weld defects based on real time observed varying weld pool surface for precision joining using gas tungsten arc welding (GTAW). However, welder behavior/adjustment appears to be a complex reactive response. To understand this complex response and develop intelligent robotic arc welding, a new scheme that correlated welding torch attitude to weld pool surface to study welder behavior in torch adjustment as response to weld pool was proposed and realized. The torch attitude and the 3D weld pool surface were synchronously measured using a wire inertial measuring unit (IMU) and a laser vision-based approach. An image processing algorithm was developed to extract the characteristic parameters of the weld pool surface from the laser stripes reflected by the specular pool surface. The improved quaternion-based unscented Kalman filter was used to estimate the torch orientation from its inertial measurement data, showing that the torch attitude has been obtained with an acceptable error in the order of 1°(x axis and y axis) and 2°(z axis). Several experiments were performed and the correlation of the corresponding data was detailed. It indicates that the change of torch attitude represents the welder's operating skills, welding experience and smart decision. The three characteristic parameters reflect the welder's reactive response on the torch adjustments. The curvature radius of laser stripes can predict the changing trends of the weld pool surface, providing the needed information for welders to make a smart decision. The proposed scheme is feasible for measuring and analyzing the welder's skills and experience.     

Dynamic nonlinear modeling of 3D weld pool surface in GTAW

As the weld pool contains abundant information about the welding process, the skilled welder always utilizes the observed information to control the weld pool to get a good weld quality. In this paper, a real-time vision sensing technology is used as the eyes of the welder to observe the gas tungsten arc welding (GTAW) pool surface and a 3D reconstruction technology is used to reconstruct the observed weld pool surface in three-dimensions. The geometric parameters are obtained from the reconstructed pool surface. Step responses are conducted to acquire the relationship between the weld pool geometric parameters and the random variation of current, and the time-domain analysis of the system is done. Various random experiments are also conducted and the dynamic relationship of the weld current and the weld pool geometric parameters is established by means of a non-linear Hammerstein model.     

基于粗定位与组件树的MAG焊图像熔池边缘提取算法

为了得到清晰、可靠的熔池边缘, 提出了一种新的基于组件树模型的MAG焊图像熔池边缘提取算法。对获取的焊缝区域图像, 使用区域粗定位方法获得关注的熔池区域, 输出标准的梯度级图像后, 使用组件树模型提取出熔池边缘。实验证明, 借助该模型不仅能够实现MAG焊图像熔池边缘提取, 并且与Sobel变换等方法相比, 有效提高了熔池边缘提取的精度。     

Control of 3D weld pool surface

The 3D weld pool surface in gas tungsten arc welding (GTAW) is characterized by its width, length, convexity and measured in real-time using an innovative machine vision system. The dynamic response of these characteristic parameters to welding current and speed as control variables is modeled. Based on the identified dynamic model, a predictive control algorithm is developed to control these characteristic parameters. The proposed algorithm is given in a closed form and no online optimization is required. Welding experiments confirm that the developed control system is effective in achieving the desired 3D weld pool surface geometry despite various disturbances.     

基于熔池背面图像的薄壁筒体熔透自整定PID控制系统

在薄壁筒体环缝焊接中,工艺参数及坡口尺寸的波动都会导致焊接熔透状态的变化.为保证稳定的熔透,需要对焊接熔透状态进行实时检测和控制.本文针对薄壁筒体环缝钨级惰性气体保护焊(TIG焊),建立了基于视觉传感的直接传感法,获取了清晰的熔池背面图像,经图像处理得到了满足熔透要求的熔池背面熔宽范围,并通过自整定PID控制器实时调节...     

分形理论在焊接图象处理中的应用

针对焊接过程中的图象处理问题，如检测焊缝边缘位置和确定焊接熔池的边缘大小和形状等问题，由于传统图象处理方法易受噪声和其他干扰信息的影响，因此很难准确及时地处理图象，为了克服此弱点，在比较了焊接过程中图象处理的微观算法和宏观算法的基础上，提出了运用分形理论的综合图象处理方法，该方法是根据焊接过程图象的特点，首先利用图象边缘的分形特征来进行区域分割，以便检测出图象边缘所在的模糊区域，然后用边缘检测方法的Laplace算子和最小二乘法拟合曲线来识别出图象的边缘，同时用该方法进行了实时焊接图象处理实验，实验结果证明，该方法不仅能准确识别出焊接焊缝或熔池图象的边缘，而且节省了图象处理的时间，为焊接过程的实时控制提供了可靠的信息。     

Intelligent modelling of back-side weld bead geometry using weld pool surface characteristic parameters

In manual welding process, skilled welders can ensure the weld quality through compensating for deviation observed from the weld pool surface. In this paper a three dimensional vision sensing system was used to mimic the human vision system to observe the three-dimensional weld pool surface in pipe GTAW process. Novel characteristic parameters containing information about the penetration state specified by its back-side weld pool width and height were proposed based on the reconstructed three dimensional weld pool surfaces. Then, variation in characteristic parameters and their relationships with the back-side parameters were studied through experiments under different welding conditions. Direct measurement of penetration is not preferred in a manufacturing site, soft-sensing method was thus proposed as an alternative to obtain it in real time due to established soft-sensing model and auxiliary variables which can be sensed in real time. In order to obtain the penetration status in real time conveniently, back-propagation neural network, principle component analysis based back-propagation neural network and global best adaptive mutation particle swarm optimization based back-propagation neural network models were established to estimate the penetration based on the proposed characteristic parameters. It was found that the top-side characteristic parameters proposed can reflect the back-side weld pool parameters accurately and the models are capable of predicting the penetration status in real time by observing the three-dimensional weld pool surface.