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.     

基于蚁群神经网络的电阻点焊工艺参数优化

提出了一种神经网络与蚁群算法相结合的08Al钢板电阻点焊工艺参数优化方法.以试验数据为样本,通过神经网络建立焊接工艺参数与焊接性能关系之间的复杂模型,利用蚁群算法对焊接工艺参数进行优化,充分发挥神经网络的非线性映射能力和蚁群算法全局寻优能力.仿真试验显示了方法的有效性和优越性.     

Artificial neural networks applied to spot welding process modeling

Artificial neural networks have been studied for applicability for modeling of spot welding process. Some basic concepts relating to neural networks are explained as well as how they can be used to model welding quality parameters in terms of the welding process parameter. The performance of the neural networks for modeling is presented and evaluated using actual welding data. It is concluded that neural network modeling is a good means of estimating spot welding quality on-line.     

基于神经网络优化的车身镀锌板点焊性能预测

针对汽车车身常用的镀锌钢板(GMW2和DP600)的点焊性能预测问题进行了研究,引入人工神经网络模型来描述点焊工艺参数空间同焊点接头质量空间的映射关系;在对普通网络存在的缺陷问题进行深入分析的基础上,结合大量试验综合考虑,对网络模型进行了优化改进;然后将试验得到的大量点焊工艺参数与相应点焊接头质量的试验数据提供给神经网络学习.结果表明,学习后的优化神经网络模型能够准确有效地预测焊接电流对点焊熔核直径、压痕深度以及拉剪强度的影响规律.亦即该优化神经网络模型可有效地实现对车身镀锌钢板点焊性能的预测,且预测精度和准确率较高,符合工程需要,具有一定的实用价值.     

Effects of modeling means on properties of monitoring models of spot welding quality

Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non-linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non-linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.     

主成分分析结合神经网络技术在焊接质量控制中的应用

介绍了主成分分析方法及人工神经网络技术在相关因素分析和质量控制的建模与估计中的应用。以大电流MAG焊熔宽控制为例，通过对6个焊接过程参数进行主成分分析，提取出影响熔宽的4个主要因素。讨论了提取的主成分与原始过程参数间的关系。以主成分得分作为新的训练样本集，送入神经网络进行计算。结果表明，基于主成分分析的神经网络无论在收敛速度，还是在训练精度上，都远远优于基本BP神经网络。     

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.     

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.     

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.     

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。     

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.     

减小点焊质量神经网络监测模型误差的措施

点焊过程监测信息与质量参数之间关系也含有较大的非线性,用线性模型去描述这样的关系将导致模型误差的增加。为了更好地描述点焊过程监测信息与质量参数之间的复杂关系,文中将神经网络理论用于点焊过程模型化。在建立点焊质量神经网络监测模型的过程中,发现,训练过程中的“假饱和”现象是减小网络模型误差的主要障碍。为此,分析了各种减小网络模型误差的可能途径,提出了相应的改善措施,并通过试验证明,所提出的观点是正确的、措施是行之有效的。     

基于人工神经网络的点焊熔核直径预测

从电极位移曲线上提取出的两个特征值及焊接能量值作为输入值,熔核直径为输出值,建立了基于BP算法的铝合金直流点焊熔核直径预测模型.该模型为隐层结点数为5的三层结构,隐层转移函数为Sigmoid函数,输出层的转移函数为线性函数.对实测结果与仿真结果进行了对比分析,结果表明,45.2%的预测值与实测值相差不超过0.5 mm,77.4%的预测误差不超过1 mm,94.3%的预测误差没有超过2 mm.回归分析结果为A=0.878T 0.982.     

基于BP神经网络遗传算法的薄板CMT点焊变形

焊接是汽车车身制造的一个关键环节,焊接质量的好坏严重影响汽车车身质量,所以焊接参数的选择至关重要. 针对薄板焊接质量控制问题,论文利用BP神经网络解决非线性问题的优势,建立焊接变形量与工艺参数之间映射关系模型;结合遗传算法构建基于遗传神经网络焊接的工艺参数优化系统;同时设计正交试验,将该方法与正交试验法相对比. 结果表明,该方法可以有效地实现CMT(cold metal transfer)点焊焊接变形预测与工艺参数优化. 通过预测模型给出合理参数,指导钢薄板和铝合金薄板的CMT点焊变形试验,提高焊接的效率.     

基于频谱分布分析及神经网络的点焊超声检测

分析了检测信号的频谱分布,采用小波分析的方法提取了能够描述点焊超声波频谱分布特征及功率特征的超声检测特征值.由这些检测特征值构成超声特征矢量,结合BP神经网络的模式识别功能对点焊直径进行精确分类与识别.与传统时域分析方法相比较,其备更高识别能力、更高的识别效率、更少的识别特征以及更小的外部干扰因素.     

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。     

热镀锌钢板连续点焊时电极损耗对焊接质量的影响

镀锌钢板电阻点焊时由于锌层的影响电极损耗非常严重.以厚0.8 mm家电用热镀纯锌钢板DX51D+Z为研究对象,探究在正交试验优化焊接参数后,热镀锌钢连续点焊时焊接接头质量变化的规律和电极损耗的特点.结果表明,当采用I=11 kA、P=2 500 N、T=13 cyc的点焊工艺参数组合可获得力学性能最佳的点焊接头;连续点焊试验初期(150点前)焊点力学强度稳定且焊点成形较好,焊接后期(150～300点)接头力学强度波动大,焊接状况不稳定;电极损耗至失效是接头质量急剧下降的主要原因,电极失效形式为头部塑性变形、端面坑蚀和Cu-Zn合金化,在本试验条件下,Cr-Zr-Cu电极焊互150点后显现不稳定现象,电极寿命约280点;失效电极端面的Cu-Zn合金层约65 μm.     

基于粗糙集的点焊多信息融合的质量判定

研究了基于粗糙集理论的在线铝合金点焊质量判定的新方法,选取焊接过程中电极位移、电极压力两个参数的八个特征量构成知识表示系统,采用自组织特征映射网络(SOM网络)离散连续属性值、基于差别矩阵的属性约简算法对获取的信息进行处理,提取判别规则,进而通过判别规则来完成焊点质量的分类.该方法不仅降低特征空间的维数、减少质量分类的工作量、降低了信息存储量,而且点焊质量判断准确率可以达到97.06%.

Abstract：

The new method of quality judgment about on-line aluminum alloy spot welding was studied, which was based on the rough set theory. The eight characters selected from two parameters (electrode displacement and electrode force in the weld process) constituted the knowledge representation system. The new method dealt with the obtained information by adopting the discretized continuous attribute algorithm with the self-organizing feature map net-work(SOM) and the attribute reduction algorithm based on discem-ibility matrix, picked up the distinguish rule, then completed the classification of spot welding quality by the rule. The method can not only reduce the dimensions of the feature space, the workload of quality classification and the information memory capacity, but also can make the accuracy of the spot weld quality judgment reach 97. 03%.     

图像处理在电阻点焊质量检测中的应用

现代制造业广泛使用电阻点焊技术,研究者长期致力于实现焊接质量快速无损检测技术的研究,本文以低碳钢为试验材料,在各种点焊工况下对焊点图像进行了采集与预处理,利用分形理论对焊点图像边缘进行检测,结果证明,利用分形几何可有效提取焊点边缘.文中研完了焊点图像特征区域面积与焊点强度之间的关系,结果表明,焊点表面图像面积与焊点强度之间呈正比例关系,可以利用焊点表面图像对点焊质量进行检测.     

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.