The axial pressure in plasma arc is measured under different conditions. Theeffects of the parameters, such as welding current, plasma gas flow rate, electrode setback and arclength, on the pressure in plasma arc are ...The axial pressure in plasma arc is measured under different conditions. Theeffects of the parameters, such as welding current, plasma gas flow rate, electrode setback and arclength, on the pressure in plasma arc are investigated and quantitative analyzed to explain therelationship between the quality of weld and the matching of parameters in plasma arc weldingprocess.展开更多
A quality monitoring method by means of support vector machines (SVM) forrobotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the weldingprocess signal, a SVM classifier is construct...A quality monitoring method by means of support vector machines (SVM) forrobotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the weldingprocess signal, a SVM classifier is constructed to establish the relationship between the feature ofprocess parameters and the quality of weld penetration. Under the samples obtained from auto partswelding production line, the learning machine with a radial basis function kernel shows goodperformance. And this method can be feasible to identity defect online in welding production.展开更多
基金This project is supported by National Defense Technology Key Lab Foundation of China(No.98JS50.3.2.JW1601).
文摘The axial pressure in plasma arc is measured under different conditions. Theeffects of the parameters, such as welding current, plasma gas flow rate, electrode setback and arclength, on the pressure in plasma arc are investigated and quantitative analyzed to explain therelationship between the quality of weld and the matching of parameters in plasma arc weldingprocess.
基金National Natural Science Foundation of China (No.59785004)Provincial Natural Science Foundation of Guangdong (No.000376)
文摘A quality monitoring method by means of support vector machines (SVM) forrobotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the weldingprocess signal, a SVM classifier is constructed to establish the relationship between the feature ofprocess parameters and the quality of weld penetration. Under the samples obtained from auto partswelding production line, the learning machine with a radial basis function kernel shows goodperformance. And this method can be feasible to identity defect online in welding production.
