The Research On Resistance Spot Welding Process And Quality Monitor Method

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. In welding process,the factors which influenced the joint quality besides craft factors,there are many fault factors ,such as voltage fluctuation ,the piece warped,the electrode assembleage is dissymmetry and so on. These fault factors lead the process instability ,then bring on expulsion ,severely arise the weld spot quality disqualification.So it is important to make sure that the welding process is stable.To diagnose fault factors is the basis of controling welding process and monitoring quality.Purposed on diagnosing resistance spot welding process and monitoring quality, the electrode voltage, welding current, electrode displacement and dynamic resistance signals were synchronously gathered. The modern analysis methods of signal are adopted to analyze 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 pattern recognition,faults diagnosis and artificial neural network methods,to diagnose the welding process and predict weld spot quality.The work was done as follows:1) A data gathering system is developed ,by which the welding current,voltage and electrode displacement signals are synchronously gathered. These signals can be used to monitor the welding process. Pretreating the welding current,electrode voltage and electrode displacement signals,to eliminate the noises,and re-protract the half-cycle signals. Time-domain method is used to analyze the signals'character.By means of half cycle current virtual value,voltage virtual value,dynamic resistance and electrode displacement signals to analyze the welding process and joint quality.2) Voltage fluctuation process in the actual product environment is simulated,then research the influence of voltage fluctuation upon the welding process signals and quality. Voltage fluctuation arises signals'strange change and quality change. The signals' strange change can be used to monitor the voltage fluctuation take place in the welding process.3)Designing natural weld test and the fault factors happen in the weldingprocess. Analyze the fault factors influence on the signals'character parameters through comparing to natural weld signals. Fault diagnosis model is constructed based on Fault Tree Analysis theory,to deduce the factors which bring the expulsion. 4)Pick-up character parameters based on dynamic resistance and electrode displacement signals. Pertinence method is used to reduce data and information,then confirm the gene which can be used to evaluate the joint quality. Fuzzy-neural network model is constructed to predict weld spot quality. By means of prediction samples'shear strength value and fact value emulation analysis results show that the model can be used to predict the spot weld quality.