电阻点焊质量在线监测方法的研究

【作者】 张宏杰；

【导师】 陈剑虹；

【作者基本信息】 兰州理工大学 ， 材料加工工程， 2005， 硕士

【摘要】 电阻点焊质量的不稳定和难以控制,严重影响了点焊技术的广泛应用,因此发展一种在线的、非破坏性的、低成本、诊断可靠性高的焊点质量评判系统对于现实生产是非常有意义的.研究表明点焊过程动态信号蕴含大量直接或间接反映焊点质量的信息,其特征分析、并行处理、信息融合是建立焊点质量监测模型,实现在线无损评估的关键。试验以焊点质量在线监测为目的,针对点焊过程电极间电压、焊接电流、电极位移和动态电阻信号,利用现代信号分析方法提取信号时域统计型描述特征,构造多信息融合的特征向量集合表征点焊过程。通过利用统计分析、模式识别、数据挖掘等领域的分析方法,建立焊点接头强度的预测、分类模型。论文工作内容包括: 搭建以AC6115AD卡,Rogowski电流传感器,DA—5型直流差动变压器位移传感器,霍尔电流、电压传感器为核心的信号采集及信号条理硬件电路;基于Visual Basic 6.0设计开发信号采集软件,实现同步无相差采集点焊过程初、次级电流、电压,电极位移信号及各监测信号的波形显示、波形编辑、数据显示及数据文件存储功能。 从时域、频域、时频分析的角度研究点焊过程焊接电流、电极间电压、动态电阻、电极位移等信号的时域、频域特征,分析认为电极间电压、动态电阻、电极位移信号能够识别分流、板厚、边距等工艺因素的变化,能够监测点焊过程喷溅现象的发生;信号频域里的变化特征不明显,因此信号特征重点将在时域中分析和提取。针对电极位移、动态电阻信号,采用动态电阻峰值时刻,电极位移峰值时刻,划分熔核形成不同阶段,设计表征焊点形成不同阶段的特征参量提取算法,通过相关分析确定表征焊点形核过程的多信息融合特征向量集合。 基于电极位移信号特征向量集合建立多元线性回归、多元非线性回归、RBF神经网络焊点接头强度预测模型,采用交叉有效性检验方法,检验预测模型的有效性。检验结果表明所建立的回归模型、RBF神经网络模型都可以用于实现对焊点质量的在线评估,RBF神经网络预测模型体现出较强的容错性和聚类性,可作为进一步研究和实现在线质量监测的方法之一。 基于电极位移、动态电阻信号监测特征向量集合,采用数据离散化技术实现点焊过程特征向量集的数据削减与泛化,通过利用较高层次数据概念替换低层次概念,建立表征不同焊接过程的模式。将不同焊接电流规范下焊点模式存储于网络,利用Hopfield网络的联想记忆功能实现对未知焊点的模式识别。Hopfield网络分类模型测试结果表明该模型可以快速实现焊点质量的在线评判和分类。 利用CART数据挖掘方法建立焊点接头抗剪强度分类、预测决策树,将焊接过程监测参量与焊点强度之间复杂的映射模型以十分直观的二叉树形式给出,用一系列监测特征参量的逻辑表达式构成接头强度分类、预测规则。CART有效性检验结果表明CART焊点质量分类、预测模型识别过程速度快,分类、预测规则易于表达,准确率高,可以较满意地完成焊点接头抗剪强度分类、预测的任务。更多还原

【Abstract】 Resistance spot welding quality is instable and difficult to control, which have influenced the wide application of resistance spot welding technology seriously .So it is necessary to develop a kind of online judging system with nondestructive , low cost and high diagnostic reliability. Lots of dynamic information, which can directly and indirectly reflect the quality of weld spot, is included within the dynamic signals of resistance spot welding process. Signal characteristic analysis, parallel treatment and information integration technology are the key to set up quality evaluation model and realize quality inspecting of weld spot online.For online monitoring and controlling quality of weld spot, the electrode voltage, welding current, electrode displacement and dynamic resistance signals were synchronously gathered. The modern analysis methods of signal were adopted to analysis the characteristics of process signals gathered, and the time-domain statistic characteristics were picked up from the four signals to set up a set of data which token the pattern of spot welding process .By means of statistical analysis, pattern recognition and data mining analytic methods, the classification and prediction models of weld spot strength were implemented. The work was done as follows.A data gathering system based on AC6115 AD card, Rogowski current transducer, DA-5 differential voltage transformer displacement sensor of direct current and Hall sensors for current and voltage was developed ,by which electrode voltage ,welding current , electrode displacement and dynamic resistance signals were synchronously gathered. The signals’ wave display, edit, signal data display and data file saving were carried out with gathering software developed by Visual Basic 6.0.Time-domain and frequency-domain methods were used to analysis electrode voltage, welding current, electrode displacement and dynamic resistance signals in this thesisdn order to research the relativity between the characters of signals and weld spot process. The preliminary analysis showed that electrode voltage, welding current, electrode displacement and dynamic resistance signals could recognize the affection of craft factor changes such as edge, diffluence and sheet materials thickness. The signals also could monitor explusion phenomenon during welding process. Because the variation of the signals gathered in frequency domain was not obvious, the signal factors were stressly analysised and picked up in time domain .Utilized the resistance and displacement signals’ parameter to devide the different phases of the nugget forming, and developed the algorithm for extracting the parameters inspected, by which a factor vector including multi-information merging could be get to express of the resistance spot welding process.Linear, nonlinear regression analysis and RBF neural network were used to predict nugget strength based on the input vector which was constructed by factors inspected of electrode displacement signal. By means of the cross-validation estimate, the linear, nonlinear and RBF neural network models were able to effectively predict the weld strength, and realize evaluating the quality of the joint online. RBF neural network model showed stronger fault-tolerant and cluster abilities, and could be regarded as the follow-up research approach.The data reduction and generalization of factors vector, which is based on resistance and displacement signals, can be accomplished through data discrete technology. When utilized the higher level data concept to replace the low level one, the pattern of different welding course could be constructed. These patterns which corresponding with the different weld joint strength were classified according to different welding current ,and realized different pattern vectors representing the different weld strength. As the states of associative memory of the Hopfield neural network, pattern vectors were stored in the Hopfield network. Depending on the ability of associative memory, the Hopfield neural network classified the pattern vec更多还原