基于信号处理对镀锌板的电阻点焊质量的研究

主要研究了点焊镀锌板过程中动态电阻和电极位移的变化趋势及与其对应关系,结果表明,动态电阻、电极位移与焊接过程中存在着直接和间接的关系。同时也研究了在同一焊接工艺参数下点焊镀锌板与低碳钢过程中的信号并加以比较,结果发现,同一点焊工艺下点焊镀锌板的焊接过程信号与低碳钢的变化趋势基本一致。提取电极位移、动态电阻的特征值,作为Som神经网络的输入特征参量,对Som网络进行归类训练,对其训练的网络进行仿真,结果表明,此网络可以对点焊接头进行分类。     

基于支持向量机的点焊质量分类

以点焊质量监控为目的,通过对点焊过程电极位移信号、动态电阻信号的同步采集和信号分析,从2种信号中提取12个有效的特征分量,构造点焊过程的监测数据集,以焊点的抗剪强度为指标,利用支持向量机(SVM)和RBF神经网络对焊点质量进行分类.测试结果表明,2种信号特征向量作为SVM的输入向量,在参数匹配的情况下,使用不同的核函数对喷溅和焊点质量分类均能取得较好的分类结果,分类器性能明显优于RBF神经网络模型.小样本情况下SVM具有较好的泛化能力,正确率仍保持在80%以上,据此认为提取电极位移和动态电阻的特征向量构筑输入向量,建立的SVM动态模型对点焊接头进行质量分类是可行的.     

基于电极电压信号的微电阻点焊质量监测分析

文中研究了微电阻点焊条件下TC2钛合金焊点质量监测问题. 首先对典型焊点的微观金相组织进行了分析,发现在熔核区域形成了针状马氏体;接着选取典型的电极电压曲线,根据其变化趋势划分为四个阶段,结合焊点的形成过程进行了相应解释,并分析了电极电压曲线特征量与熔核直径之间的相关性;最后应用BP神经网络,采用相关性较强的电极电压特征量及焊接电流作为神经网络输入,选取熔核直径作为神经网络输出,结合试验数据. 结果表明,通过神经网络训练及测试,有效地实现了对焊点质量的可靠预测.     

一种镀锌板电阻点焊质量的无损评判方法

探索了一种以点焊表面数字图像为信息源的质量评判方法.获取镀锌板点焊接头表面的图像,通过对不同焊接规范下焊点的表面图像分析,发现焊点表面图像可以被分为几个不同的特征环区;研究焊点表面图像特征环区与接头质量的相关性,根据特征区域面积与焊点抗剪强度的相关性分析结果,选择了相关性显著的特征参数作为输入向量,接头的抗剪强度作为输出参量,建立了一种以剪切强度为评判指标的BP神经网络模型.试验结果表明:基于图像信息处理实现镀锌板焊点质量无损监测的方法切实可行.     

基于电极位移信号的电阻点焊质量预测模型

设计了电阻点焊过程动态参数监测系统，采集焊接电压、电流以及电极位移信号，建立以交流电阻点焊过程中的周波参数为输入空间，以试样的抗拉剪切强度为输出空间的点焊质量预测神经网络模型．为实现电阻点焊质量在线监控奠定了理论基础。试验结果证明了RBF神经网络用于这类应用场合的可行性和有效性。     

不锈钢的电阻点焊

对不同厚度及种类冷轧和热轧不锈钢进行了点焊性能试验,重点探讨了马氏体、铁素体不锈钢的点焊可焊性,从微观金相组织和接头硬度分布的两个角度分析了马氏体、铁素体不锈钢的点焊过程,并经大量试验提出了提高马氏体、铁素体不锈钢点焊接头抗剪强度的有效方法。     

电阻点焊质量的微处理机控制

对点焊过程中的电极电压变化规律与焊点尺寸的关系进行了分析研究,提出了用“相对质控电压”作为合格焊点形成标志,以实现焊点尺寸自适应控制的方法。对以此为基本原理研制的KD3—200型微机点缝焊控制器的硬件结构和软件特点作了论述。     

电阻点焊接头外观缺陷的诊断模型

电阻点焊过程中的喷溅、电极头粘损等现象,是导致其接头外观质量不合格的主要因素.以获取的点焊接头表面数字图像为信息源,通过对发生喷溅和粘损情况下的图像分析,提取了焊点图像的周长L,面积S0,伸长率A和致密度C等作为反映图像特征的参量.研究了这4个参量随焊接工艺参数变化的规律,选取了L,S0及A作为判识其外观缺陷的特征参量...     

电阻点焊质量监测

电阻点焊质量监测作为一种无损质量检测方法,近年来受到了国内外学术界的广泛关注。阐述了电阻点焊质量监测系统的结构与组成,详细介绍常用的检测信号及其传感器,总结传感器、监测方法及监测系统的最新成果和发展趋势。     

基于嵌入式系统的电阻点焊飞溅信息在线判读

钢材的电阻点焊过程中,动态电阻的变化表征了丰富的焊接质量信息.采用嵌入式系统实时监测焊接过程动态电阻的变化有利于提高对焊接质量的认识.设计了嵌入式监测系统,系统同时监测焊接电流、电极间压力和电极间的电压、电极位移信号.分析了双相钢DP600焊接过程中的动态电阻变化与焊点熔核之间的关系,并阐明用焊点两端的电压信号替代动态...     

Quality estimation of resistance spot welding of stainless steel based on BP neural network

The end value of the dynamic resistance curve of stainless steel was proved to have strong correlation with nugget size by experiments, so it was an important factor for estimation of weld quality. BP neural network was employed to estimate the weld quality. The end value of the dynamic resistance curve, welding current and welding time were selected as the input variables while the nugget diameter, which is closely related to weld quality, was selected as the output variable. Testing results shows that such network has fine fault tolerance and real-time quality estimation is possible.     

电阻点焊质量在线无损估测

采用动态电阻法建立了低碳钢电阻点焊熔核直径的人工神经网络模型,该模型为3层BP向前网络结构,通过大量正交试验训练获得其模型数据。将模型数据移植到C语言中,建立了熔核直径在线估测系统。该系统有望应用于实际生产的质量监控。     

基于核Fisher判别分析的点焊质量评估

通过对点焊过程电极位移信号的实时采集和特征分析,利用模糊化方法将电极位移信号映射为15×25模式矩阵.依据不同焊接电流参数下位移信号的模式特征将位移信号的模式分为5类,分别对应不同的焊点质量.以不同类别焊点的模式矩阵构建输人数据空间,利用核Fisher判别技术将低维数据空间的非线性分类问题转化为高维特征空间的线性分类问...     

On-line evaluating on quality of mild steel joints in resistance spot welding

A method was developed to realize quality evaluation on every weld-spot in resistance spot welding based on information processing of artificial intelligent. Firstly, the signals of welding current and welding voltage, as information source, were synchronously collected. Input power and dynamic resistance were selected as monitoring waveforms. Eight characteristic parameters relating to weld quality were extracted from the monitoring waveforms. Secondly, tensile-shear strength of the spot-welded joint was employed as evaluating target of weld quality. Through correlation analysis between every two parameters of characteristic vector, five characteristic parameters were reasonably selected to found a mapping model of weld quality estimation. At last, the model was realized by means of the algorithms of Radial Basic Function neural network and sample matrixes. The results showed validations by a satisfaction in evaluating weld quality of mild steel joint on-line in spot welding process.     

基于焊点表面图像处理的点焊质量监测

以采集的电阻点焊接头表面的数字图像作为信息源,探索了一种新的点焊质量无损监测方法.首先,通过图像特征分析,焊点表面图像被划分为4个环形特征区域,提取环形特征区域面积作为表征焊点质量的特征参数.其次,根据特征区域面积与焊点抗剪强度的相关性分析结果,选择了相关性显著的3个特征参数作为输入向量,焊点抗剪强度作为输出向量,建立了点焊质量的RBF神经网络监测模型.仿真分析和验证结果表明,基于焊点表面图像特征信息处理监测点焊质量的方法是可行的.     

Modeling and analysis of resistance spot welding based on neural network

A numerical study on the multi-parameter control method based on nonlinear auto-regressive with exogenous input neural network (NARX) is presented here. Welding current was set as the input parameter; electrode displacement and dynamic resistance were set as the output parameters. The NARX model using these parameters was set up to simulate the multi-parameter resistance spot welding process. By comparing actual experimental data and NARX model output data, it was validated that the results from the model reflect the relationship between input parameter and output parameters correctly under the influence of many affecting factors.     

Filling technique for keyhole of friction stir spot welding based on the principles of resistance spot welding

Keyhole at the end of a weld prepared by friction stir welding(FSW) is one of the major issues that impede the application of FSW. To address this issue, a keyhole filling technique was proposed in this paper, which is based on the principles of resistance spot welding(RSW). A three-phase secondary rectifier resistance spot welder was applied as the experimental instrument for filling the keyhole in the center of friction stir spot weld(FSSW). The test sheet is a 2024-T4 aluminium alloy with a thickness of 6.0 mm. The experiments results show that the filled joint strength is improved by 26.12% since the area is increased for the plug in the keyhole. And there are two kinds of dimples in the tensile fracture—equiaxial dimples and long dimples. The filled joint involves the fusion welding zone(FWZ), pressure welding zone(PWZ), melted plug zone(MPZ), and plastic deformation zone(PDZ). The FWZ and the PWZ is the melting bond and diffusion bond between the plug and keyhole, respectively. The MPZ is the center part and the PDZ is upper or lower part of the plug.     

基于BP神经网络的DP600点焊熔核参数建模

以焊接电压、焊接电流构造输入向量,熔核直径、热影响区外径和焊接区焊后厚度为输出量,建立DP600高强钢电阻点焊的熔核参数模型。推导了梯度下降法、动量梯度法和共轭梯度法三种权值算法,并用实际试验数据对模型进行训练和预测。结果表明,共轭梯度法训练后的预测结果误差率最低,所有参数的误差在8%内,平均误差在4%内,可用于在线检测来提高产品质量。     

继电器引线的逆变电阻点焊质量智能监测

采用了人工神经网络技术对继电器制造中铜线在磷铜片上的点焊进行了质量监测.利用BP(back propagation)神经网络模型及其算法,建立以焊接电流和电极间电压作为输入参量、焊点拉剪强度作为输出参量的神经网络质量监测模型.在Matlab中对不同隐层节点和转移函数的模型进行仿真,选择合适的隐层节点数和转移函数.在逆变电阻点焊机上进行了试验验证,表明所建立的人工神经网络质量监测模型的精度能满足工程应用的要求.