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Intelligent constant current control for resistance spot welding
Resistance spot welding is one of the primary means of joining sheet metal in the automotive industry and other industries. The demand for improved corrosion resistance has led the automotive industry to increasingly use zinc coated steel in auto body construction. One of the major concerns associated with welding coated steel is the mushrooming effect (the increase in the electrode diameter due to deposition of copper into the spot surface) resulting in reduced current density and undersized welds (cold welds). The most common approach to this problem is based on the use of simple unconditional incremental algorithms (steppers) for preprogrammed current scheduling. In this paper, an intelligent algorithm is proposed for adjusting the amount of current to compensate for the electrodes degradation. The algorithm works as a fuzzy logic controller using a set of engineering rules with fuzzy predicates that dynamically adapt the secondary current to the state of the weld process. The state is identified by indirectly estimating two of the main process characteristics - weld quality and expulsion rate. A soft sensor for indirect estimation of the weld quality employing a learning vector quantization (LVQ) type classifier is designed to provide a real time approximate assessment of the weld nugget diameter. Another soft sensing algorithm is applied to predict the impact of changes in current on the expulsion rate of the weld process. By maintaining the expulsion rate just below a minimal acceptable level, robust process control performance and satisfactory weld quality are achieved. The intelligent constant current control for resistance spot welding is implemented and validated on a medium frequency direct current (MFDC) constant current weld controller. Results demonstrate a substantial improvement of weld quality and reduction of process variability due to the proposed new control algorithm.
Intelligent constant current control for resistance spot welding
Resistance spot welding is one of the primary means of joining sheet metal in the automotive industry and other industries. The demand for improved corrosion resistance has led the automotive industry to increasingly use zinc coated steel in auto body construction. One of the major concerns associated with welding coated steel is the mushrooming effect (the increase in the electrode diameter due to deposition of copper into the spot surface) resulting in reduced current density and undersized welds (cold welds). The most common approach to this problem is based on the use of simple unconditional incremental algorithms (steppers) for preprogrammed current scheduling. In this paper, an intelligent algorithm is proposed for adjusting the amount of current to compensate for the electrodes degradation. The algorithm works as a fuzzy logic controller using a set of engineering rules with fuzzy predicates that dynamically adapt the secondary current to the state of the weld process. The state is identified by indirectly estimating two of the main process characteristics - weld quality and expulsion rate. A soft sensor for indirect estimation of the weld quality employing a learning vector quantization (LVQ) type classifier is designed to provide a real time approximate assessment of the weld nugget diameter. Another soft sensing algorithm is applied to predict the impact of changes in current on the expulsion rate of the weld process. By maintaining the expulsion rate just below a minimal acceptable level, robust process control performance and satisfactory weld quality are achieved. The intelligent constant current control for resistance spot welding is implemented and validated on a medium frequency direct current (MFDC) constant current weld controller. Results demonstrate a substantial improvement of weld quality and reduction of process variability due to the proposed new control algorithm.
Intelligent constant current control for resistance spot welding
El-Banna, M. (author) / Filev, D. (author) / Chinnam, R.B. (author)
2007
8 Seiten, 11 Quellen
Conference paper
English
| British Library Online Contents | 2007