基于信号特征分析的点焊质量在线监控方法的研究

【作者】 种玉宝；

【导师】 马跃洲；

【作者基本信息】 兰州理工大学 ， 材料加工， 2004， 硕士

【摘要】 电阻点焊质量在线监控是汽车装配生产中亟待解决的问题。电阻焊过程中电流、电压信号既反映输入能量，又反映熔核形成过程接头阻抗的动态变化。影响焊接质量的各种随机因素，均直接或间接地体现在电流、电压等信号的变化中。但由于影响因素的不确定性、非线性和相互耦合，故不能直观地从波形中加以观测。因此，本课题以电阻点焊质量在线监控为目的，电流、电压等焊接动态信号为对象，用现代信号分析方法研究信号的时频特征，提取信号中隐含的信息。通过建立人工神经网络模型来预测点焊接头熔核尺寸。论文工作主要包括以下内容：研制了以KS2062型A／D卡为核心的数据采集系统，对点焊焊接过程中的电压、电流等信号进行同步采集，采集系统软件由C++语言编写，能够显示信号波形，并进行预处理。利用时域、频域、小波等多种方法对电流、电压等信号进行分析，研究信号特征及其与电阻点焊过程的相关性。从信号分析角度丰富对焊接过程的认识，为焊接缺陷在线识别和质量分类奠定基础。初步分析认为，电流、电压信号波形以及接头动态电阻、加热功率的变化与熔核形态密切相关，可用于电阻点焊接头质量的在线监控。电压、电流信号在频域里的变化特征不明显，因此信号特征重点放在时域中分析和提取。采用归一化处理后的周波参数时间序列构造网络输入向量，尝试两种不同的网络对点焊接头熔核尺寸进行预测。对普通BP算法运算速度慢等缺点提出分析并提出了相应的改善方法，大大提高了模型的运算速度和精度，较好的预测了熔核尺寸。RBF网络是一种通过改变神经元非线性变换函数的参数以实现非线性映射，从而大大加快学习速度并避免局部极小问题。另一方面，该网络运用了多变量插值的径向基函数方法，使网络能实现高维空间的分类。因此，RBF网络在焊接质量在线监控方面具有应用价值。更多还原

【Abstract】 The on-line quality monitoring of spot welding is desiderated in automobile assembly process. In the process of resistance welding, the signals of voltage and current reflect both the input energy and the dynamic variation for joint impedance of forming the nugget. The random influence factors of welding quality must be present in the variety of welding signals directly or indirectly, but can’t be observed simply because of its indetermination, non-linearity and coupling with each other. Purposed on on-line monitoring and controlling of resistance spot welding quality,the modern analysis methods of signals were adopted to analyse the characteristcs of dynamic welding current and voltage, and picked up the informations in them. Artificial Neural Networks(ANN) were used to predict the nugget sizes of resistance spot welder. The work was done as follows.A signal collection system was developed with KS2062. The welding voltage and current were measured synchronously, which carried out displaying and presetting with the C+ +. Time-domain, frequency-domain and wavelet were used to analyse current, voltage, displacement signals in this thesis in order to research the relativity between the character of signals and the process of resistance spot welding. Enriched the recongition of the process of welding by the point of view signal analysis, the basic will be established for welding defect on-line identifying and quality classifying. It is concluded that the waveform of current, voltage signals and the variation of dynamic resistance, power of joint have related to nugget formation closely, so they can be used for on-line monitoring and controlling of joint quality of resistance spot welding. Because the variation of the signals of voltage and current is not obvious in frequency domain, the signal characters were stressly analysed and picked up in time domain.The two different neural networks were used to predict the nugget sizes of resistance spot welder, in which the input vectors were constructed by the time sequences of cycle parameters normalized. The common BP Network was analysed. The effective methods were advanced to improve the rapidity and accuracy of the model, which have predicted nugget sizes availably. The Radial Basis Function (RBF) neutral network is a non-linear map by altering the parameters of non-linear activation functions of neuron so that it can improve the rapidity of networks and avoid the local minimality. On the other hand, the RBF neutral network can realize the classification of multi-dimension because it adopts the multivariate interpolation way based on the Radial Basis Functions. Therefore, the RBF neutral network will act a significant part in on-line monitoring and controlling of welding quality.更多还原