Research On Methods Of Quality Detection Of Aluminum Spot Welding Based On Reverse Process

Because of the characteristic of high productivity and automatic realization, resistance spot welding, as a common welding method, has obtained widespread application in the production of automobile and aviation industry. It has more application especially in the rapid development of automobile industry. The concept of automobile lightweight makes the aluminum alloy, which is one of the light alloy with excellent performance, to be the leading material in the automobile production due to the lack of energy in the world. The study of welding quality in resistance spot welding of aluminum alloy becomes a important study direction as a result of the poor welding performance of aluminum alloy and the complicated influential factors of spot welding quality. At present, it is insufficient in the two aspect of research on quality of spot welding, one is feature extraction, now the emphasis of feature extraction in spot welding is put on the extraction of the macroscopic feature and singularity, due to the process of spot welding is influenced by a large number of factors, the characteristic signal of welding spot is interfered by noise and changed. But the common method can not extract the change of feature of signal precisely. The other deficiency is existed in detection method, now the quality detection model of welding spot is emphasized on the model construction based on relation of feature between signal and nugget quality, but the deficiency of spot welding is influenced by a large number of factors, there is not a one to one correspondence relation between the deficiency of nugget and spot welding signals, as a result the above reason lead to the low percentage of accuracy of detection of detection model of spot welding, though the theory of pattern recognition has been introduced to quality detection of spot welding by some researcher, the application of this method should be researched in profound direction. The below work is done aim to the deficiency existed in present detection method of spot welding:(1) The data acquisition system based on multi-information fusion is constructed, and four sync signals is obtained.The data acquisition is used in dissertation to collect the four variables: electrode voltage, welding current, welding sound and electrode displacement. This system based on multi-information fusion can complete independently data collection, A/D transfer and data local save. Each intelligent terminal communicates with computer by digital management module. The signals can be collected by the data acquisition system based on multi-information fusion quickly efficiently and synchronously. (2) The eigenvalue of signals is extracted using theory of signal analysis and nonlinear dynamics.Because the change of signals is complex and the amplitude narrow in the process of spot welding, the common extraction method will be inaccuracy, based on the theory of signal and nonlinear dynamics, considering the better noise immunity and express ability to change feature of signal as a whole, the fractal dimension from the signals of electrode voltage, welding current, welding sound and electrode displacement are extracted in the paper; according to the input feature of energy of voltage to nucleation of spot welding, the high-order spectrum is used to analysis the voltage signal, and the five max spectral line of line spectrum of three-order cumulant slice of voltage signal. Through analyzing to non-linear feature of welding current, the chaotic characteristic is discovered, and the chaotic characteristic can reflect the weak change of signal, Lyapunov exponent is the characterization value of chaotic characteristics of signal, because of the great effect of current signal to nugget size and spatter, the Lyapunov exponent of current signal is extracted as the second feature. In the process of spot welding, nucleation and spatter can emit sound, so the change rate of energy of sound is extracted as the second feature of sound. The change trace of electrode displacement can be greatly influenced by nucleation and spatter, the Renyi entropy, which can attribute the uncertainty of electrode displacement, is extracted as the second feature of electrode displacement signal. as a result, each spot weld can correspond to 8 features.(3) The fuzzy grey information system, the support vector machine and the hidden Markov chain are introduced to the quality detection of resistance spot welding by using the artificial intelligence theory, pattern recognition theory and application mathematics knowledge. The detection effect of each model and the influence of each data feature which is extracted in this paper on the percent of accuracy of detection are discussed.The fuzzy grey information system is introduced to the field of quality detection of resistance spot welding for the first time. According to the concept of fuzzy-gray relational degree, the fuzzy grey information system model of resistance spot welding is constructed, and effects of every signal characteristic on the percent of accuracy of detection of the model is discussed. The fuzzy grey information system has a better detection effect on defects of resistance spot welding. When the eight eignvalues are inputted, the maximum percent of accuracy of detection is obtained, the percent of accuracy of detection of nugget size is 91% and the percent of accuracy of detection of spatter is 92%. The support vector machine model of resistance spot welding signal is constructed and the effect of each feature on the detection accuracy is analyzed. The optimal detection result of nugget size is 86% when five or six eignvalues are inputted in the support vector machine model. The optimal detection result of spatter is 88% which can be achieved when five eignvalues are inputted. The hidden Markov chain model of resistance spot welding is constructed for the first time and effects of every feature combination on this model is analyzed. The optimal detection result of nugget size is 93% and the optimal detection result of spatter is 87%, both of which can be achieved when seven eignvalues are inputted.(4) In order to inspect nugget size and spatter simultaneously, the model array of nugget size detection and spatter detection is constructed.An detection model array is constructed which is composed of an eight eignvalues fuzzy grey system nugget size detection model and a six eignvalues fuzzy grey system spatter detection model, and the optimal detection effect can be increased to 89%. The optimal percent of accuracy of detection of the support vector machine model array is 80% and the optimal percent of accuracy of detection of the hidden Markov chain model is 81%. Through comparison, it has been found that the fuzzy grey information system inspection model is the best resistance spot welding defects detection model, another two methods can be used as assistant detection methods.