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基于信息融合技术的焊点质量评估 总被引:2,自引:0,他引:2
基于点焊焊接过程电极位移、动态电阻信号的同步采集和特征分析,从2种信号中提取若干特征参量,依据特征参量与焊点接头抗剪切力间的相关性分析结果,选取特征参量建立数据集,利用多元线性、非线性、支持向量机统计分析方法实现多信息融合,构建焊接过程监测参量与焊点强度之间的回归映射模型.进而实现对未知焊点样本强度的预测.交叉有效性检验结果表明以相关性显著的特征参量建立的多元线性回归、非线性回归、支持向量机回归预测模型,对于评估焊点质量是有效的,其中支持向量机回归预测有效性最为显著,可作为进一步研究和实现在线质量监测的方法. 相似文献
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电阻点焊过程动态信号蕴含大量直接或间接反映焊点质量的动态信息,通过对焊接过程电极位移、动态电阻信号的同步采集和分析,从两种信号中提取了12个特征参量,建立表征点焊过程的数据集,以焊点抗剪强度作为焊点质量评价指标,利用支持向量机(SVM)统计学习方法,将焊点试样动态监测参量与焊点抗剪强度之间低维的复杂非线性映射关系,映射到一个高维的特征空间(Hilbert空间),原试样数据空间的非线性关系相应变化为高维特征空间的线性关系,在不增加计算复杂度的情况下,实现对未知焊点试样抗剪强度的分类及预测。SVM测试结果表明,支持向量机在小样本情况下具有较好的泛化能力,分类、预测速度快,准确率高,能较为满意地完成焊点强度的分类、预测任务,可以作为进一步研究的方法。 相似文献
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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. 相似文献
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针对电阻点焊过程非线性、多变量耦合和存在随机不确定因素的特点,采用正变试验设计方法科学安排点焊试验,研究电极压力、焊接电流和焊接时间对201不锈钢点焊接头拉剪强度及焊点熔核直径间的关系.研究结果表明,焊接电流对焊点质量影响最大,电极压力次之,焊接时间的影响最小.试验范围内的最佳焊接规范参数为:焊接电流6.4 kA、电极压力3.6 kN、焊接时间8周波,点焊接头拉剪强度可达8.92 kN.201不锈钢点焊接头中的主要焊接缺陷为缩孔和结晶裂纹,采用较大的电极压力和较小的焊接电流焊接可减少焊接缺陷的形成,使接头承载能力明显提高. 相似文献
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以新型双相高强钢为研究对象,采用不同的工艺参数进行正交试验,并对点焊接头进行拉伸测试,分析各因素对电阻点焊质量的影响;然后对点焊接头金相组织进行观察,分析新型双相高强钢点焊接头的失效模式和接头金相组织特征。结果表明,点焊接头抗剪载荷的最优工艺参数为:焊接电流8 k A,焊接时间15 cyc,电极力2k N,此时点焊接头的最大剪切力达到最大值。优质焊点在拉剪试验中最先从熔核边界附近开裂,随后延伸至母材部分,直至点焊接头全部断开。 相似文献
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采用交流电阻点焊焊接了AM50镁合金板材,分析了点焊接头的力学性能、显微组织和断口形貌。结果表明:接头的抗剪强度随着电极压力、焊接电流以及焊接时间的增大均为先逐渐增大,待达到最大值后.又开始逐渐减小;板材表面清理状态对接头的质量有很大影响,未经过清理的试件接头强度较低;机械打磨的试件总体焊接质量较好;采用化学清理的焊件,接头质量稳定,强度随焊接电流增大逐渐提高。点焊接头的金相组织主要由等轴晶构成。断口分析显示延性断裂特征。 相似文献
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基于PCA-SVM方法的点焊质量评估 总被引:1,自引:1,他引:0
通过对电阻点焊过程电极位移和动态电阻信号的实时采集和时域特征分析,利用电阻信号动态特征刻画熔核形成不同阶段,从同步电极位移信号中提取9个特征参量建立输入样本数据集.以焊点接头抗剪强度作为焊点质量的评价指标,将PCA(主成分分析)方法与传统的SVM(支持向量机)回归分析相结合,利用PCA方法对支持向量机的输入样本数据集进行主成分分析,消除了输入特征参量间的自相关性,实现数据降维后作为支持向量机的输入,建立焊点质量映射模型.交叉有效性预测结果表明,基于PCA-SVM的算法增强了SVM的泛化能力,比传统的SVM算法具有更高的预测精度. 相似文献
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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. 相似文献
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This study focuses on weld quality evaluation by electrode voltage in small-scale resistance spot welding of titanium alloy. Voltage curve could be divided into four stages based on the variation characteristic. The single voltage peak was detected as combined effects of increasing bulk material resistivity and nugget size. Variations of voltage curve shape, voltage peak and failure load were more sensitive to welding current than electrode force. A generalised regression neural network was proposed to evaluate weld quality using features extracted from voltage signal. A discrete Hopfield neural network was also applied for electrode voltage recognition. The recognised voltage patterns were found effective in identifying different quality levels. A real-time and on-line quality monitoring system could be developed. 相似文献
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分别对1.5 mm厚的钛合金板进行胶接点焊和电阻点焊连接,获得了不同焊接电流下的胶接点焊和电阻点焊接头,从熔核的C扫描图像、接头的失效载荷和断口形貌等方面,对比分析了胶接点焊和电阻点焊的接头强度及失效样貌. 结果表明,通过观察A扫描信号的变化与C扫描图像的特征,能够很好的划分接头的热影响区、熔合区、熔核区以及检测出接头的熔核直径和焊接缺陷. 随着焊接电流(7.0~10.0 kA)的逐渐增大,接头熔核直径及失效载荷呈递增趋势;当焊接条件相同时,胶接点焊接头的熔核直径普遍大于电阻点焊接头,但接头的强度相当. 当电流在7.0~8.5 kA时,接头强度不足,熔核区的断口处出现大小不等的韧窝,呈现出韧性断裂特征;当电流为10.0 kA时,接头强度较高,主要呈现出韧性断裂与准解理断裂特征. 相似文献
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H. J. Zhang F. J. Wang W. G. Gao Y. Y. Hou 《Science & Technology of Welding & Joining》2014,19(3):242-249
To improve the efficiency of acquiring monitored features and present a reliable quality assessment method for resistance spot welding, a novel method for converting the electrode displacement signal into binary image is proposed. The probabilistic neural network is adopted to provide a probabilistic viewpoint and a deterministic classification result of weld quality when the image characteristic of displacement signal is selected as monitored features. Test results of the classifier demonstrate that it is feasible and reliable to utilise binary image of the displacement signal to evaluate weld quality. The method reserves weld quality information as much as possible and it avoids complex algorithm for extracting and selecting monitored features. At the same time, when there are small samples, the classifier can identify good or bad weld rapidly and accurately even though the weld is from abnormal welding process, such as expulsion, current shunting and small edge distance condition. 相似文献
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针对钢轨闪光对焊的特点,根据GAAS80/580焊机记录的压力、电流和动端位移随时间而变化的曲线,从中提取了10个主要影响接头灰斑面积的特征参数作为BP神经网络预测模型的输入量,建立了钢轨闪光对焊接头的灰斑面积预测模型.采用粒子群算法优化了BP神经网络的权值和阈值,并利用优化后的BP网络模型对接头灰斑面积进行了预测.结果表明,提取的特征参数能较好地反映焊接接头灰斑情况,粒子群算法优化的BP神经网络预测模型能较准确地预测出焊接接头灰斑面积. 相似文献
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采用不同焊接工艺对TRIP980钢板进行点焊试验,研究了焊接电流、焊前预热及焊后热处理工艺对点焊性能的影响. 结果表明,随着焊接电流的增大,焊点的熔核直径和拉剪力均增大,但当电流过大而发生飞溅时,焊点的熔核直径和拉剪力开始减小. 焊前预热工艺可提高点焊飞溅电流,进而可以获得更大的熔核直径及拉剪力. 在对焊点进行焊后热处理的情况下,当焊接电流与焊后热处理电流之间的冷却时间超过900 ms时,可显著改善熔核组织,降低熔核硬度,提高焊点拉剪力. 相似文献
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《Science & Technology of Welding & Joining》2013,18(3):253-259
AbstractWeld joint dimensions and weld metal mechanical properties are important quality characteristics of any welded joint. The success of building these characteristics in any welding situation depends on proper selection-cum-optimisation of welding process parameters. Such optimisation is critical in the pulsed current gas metal arc welding process (GMAW-P), as the heat input here is closely dictated by a host of additional pulse parameters in comparison to the conventional gas metal arc welding process. Neural network based models are excellent alternatives in such situations where a large number of input conditions govern certain outputs in a manner that is often difficult to adjudge a priori. Six individual prediction models developed using neural network methodology are presented here to estimate ultimate tensile strength, elongation, impact toughness, weld bead width, weld reinforcement height and penetration of the final weld joint as a function of four pulse parameters, e.g. peak current, base current, pulse on time and pulse frequency. The experimental data employed here are for GMAW-P welding of extruded sections of high strength Al–Zn–Mg alloy (7005). In each case, a committee of different possible network architectures is used, including the final optimum network, to assess the uncertainty in estimation. The neural network models developed here could estimate all the outputs except penetration fairly accurately. 相似文献