| With the rapid development of urban rail transit, the manufacturing of stainless steelurban rail car which is one of the mainstream vehicle models, develops rapidly as well. Thejoining of the stainless steel car body mainly adopts the resistance spot welding technologyto achieve design goals of lightweight, etc. In the welding process, defects like spatter,incomplete weld or loose weld are easy to appear because of its high heating intensity andshort welding time, which affects the stability of welding quality and the quality of thestainless steel body. At the same time, the formation process of the spot welding nugget isin the closed state, which cannot be observed directly and cause great difficulty in the spotwelding quality control. Therefore, the technology of the quality control and evaluation inthe spot welding has always been paid close attention to. The significance of this studybecomes more important as the requirement of high-speed railway vehicles andmanufacturing quality are increasing continually.There are mainly two kinds of quality evaluation methods used commonly in theresistance spot welding at home and abroad. The first method is online evaluation based onwelding process parameters. It predicts the welding quality mainly through themathematical model which is established by the relationship between the joint quality andthe characteristic parameters like the welding current, the dynamic resistance, the voltagebetween electrodes, the electrode pressure or the electrode displacement. However, sincethe resistance spot welding is a nonlinear and multivariable coupling complex process witha large number of uncertain factors influencing each other, the method described above canonly provide some reliable quality information, but cannot be used to accurately evaluatethe welding quality. Because of the extreme operating conditions and almost ideal demandof the mathematical model, the research of this method is still in the laboratory stage, andcannot be applied to the actual welding production.The second method is destructive test after welding. This method need to peel and reverse the spot welds to obtain general information of the nugget diameter and jointstrength, and then to evaluate the welding quality with reference to relevant standards. Atpresent, this method is mostly adopted in enterprises to debug the spot weldingspecifications and carry out the random sampling inspection in the production process. Butthis method is of low efficiency, and it also wastes materials and increases the human andproduction costs. So, this method is not suitable as the on-line detection means to evaluatethe resistance spot welding quality. In recent years, many scholars are devoted to thenondestructive detection technology of the resistance spot welding quality. Comparing withother nondestructive detection methods, the ultrasonic detection draws more attention withthe advantages of high sensitivity, wide test range, fast test speed, good safety performance,and easy for site operation.At present, there are still many problems to be solved urgently in the ultrasonicinspection technology for the resistance spot welding, such as how to distinguish the coronabond from the nugget to obtain the accurate nugget diameter and improve the detectionaccuracy, how to quickly evaluate the spot welding quality and improve the detectionefficiency, and how to realize the intelligent detection of welding defects. These problemsgreatly limit the application of the technology in the actual industrial production. In order tomeet the demand of car body’s security and stability and promote the development ofultrasonic nondestructive detection technology for the resistance spot welding, this paperstudies the quantitative and intelligent ultrasonic detection for the resistance spot weldingof stainless steel, which has important significance in the theory and engineeringapplication.This paper makes a research on a portable spot welding automatic ultrasonic detectionsystem. The system is portable and reliable, and it is not only simple to operation but alsorealizes the function of self-adaption scanning detection, real-time signal acquisition andC-scan images automation generation without the probe submerged in the water, whichprovides the reliable data sources for the quantitative and intelligent ultrasonic detection forspot welding quality. In this paper, the ultrasonic reflection coefficient model in a medium is constructedthrough studying the characteristics of the ultrasonic propagation. It reveals that in a certaingap thickness range, the acoustic pressure reflection coefficient shows frequency-dependent,which provides a research thought for testing spot welding defects and especiallyestablishes a theoretical base for distinguishing the weak bonding. The study analyze theultrasonic A-echo signal of the spot welding joint in time domain, frequency domain andtime-frequency domain respectively and it is found that the ultrasonic echo signal has acertain corresponding relationship with the internal fusion state of the spot welding joint.The characteristic signal which can represent the spot weld is extracted and thehigh-frequency ultrasonic echo signal is separated using the wavelet packet transform toeffectively identify the nugget and the corona bond according to the spectrumcharacteristics, and the accurate nugget diameter is finally obtained.The stepping mechanical scanning detection is carried out on the spot weldingspecimens with the scanning step length1000μm. The16MHz high frequency signal whichcan represent the internal fusion state of the welding joint is extracted by the wavelet packettransform to get the low resolution C-scan images in the whole scanning area. The methodof the bicubic image interpolation, which subdivides and reconstructs the C-scan images,enhances the image resolution, and the detection efficiency is also improved14times. Itoffers the possibility of realizing the rapid spot welding ultrasonic C-scan detection and hasvery positive significance in the practical engineering applications. After the bicubic imageinterpolation of the C-scan images, the computer image enhancement and edge detectionprocessing are conducted to get the clear nugget boundary topography, which furtherhighlights the nugget features and greatly reduces the amount of computation. Then thespot welding nugget diameter is automatically obtained using the equivalent diameteralgorithm procedure, and the automatic and quantitative ultrasonic detection andassessment for the spot welding quality is finally realized. Through the metallographicexperimental verification, the testing values of the nugget diameter fit the actual measuredvalues well, and both the mean value and the variance of the normal distribution of relative error are very small. The detection method is of high accuracy and excellent stability. It canbe used to evaluate the quality of resistance spot welding and is more reliable than thesingle tensile destructive test.The simulated calculation is carried out on the ultrasonic detection process forstainless steel resistance spot welding by the finite element technology. The calculationreveals the ultrasonic propagation characteristics and the scattering regularity in the inner ofthe spot welding joint, which provides the theoretical basis for exploring the method ofnugget edge recognition and quantitative nugget diameter calculation. Then the weldingporosity which easily appears in the spot welding nugget is simulated, and the effect of itsdifferent depth on the ultrasonic propagation characteristics is studied. The correctness andvalidity of the simulation and the model are verified by the experimental method. Thesimulation method provides very useful information for the waveform analysis during thetesting process and offers experimental samples for the defect recognition.In this research, four kinds of resistance spot welding specimens, including loose weld,incomplete weld, shrinkage cavity and normal spot weld are tested by the developedultrasonic detection system. The characteristic signals which can reflect different spotwelding types are extracted. Characteristic signals of welding defects are automaticallyrecognized and classified on the computer by BP neural network, and the correctrecognition rate reaches more than96%, which realizes the intelligent ultrasonic detectionfor resistance spot welding quality of stainless steel. |
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