AC currents are automatically calibrated by two different thermal current converter(TCC)designs.The two designs are different in the used number of the thermal-elements(TEs).Consequently they differ in their output el...AC currents are automatically calibrated by two different thermal current converter(TCC)designs.The two designs are different in the used number of the thermal-elements(TEs).Consequently they differ in their output electro motive force(EMF).Studying the effect of changing the output EMF is done in this paper through calibrating AC currents.5 mA and 5 A are accurately calibrated at different frequencies 55 Hz,1 kHz and10 kHz by the two TCCs.A comparison is made between the results to evaluate the effect of the output EMF value on the accuracy and the uncertainty of the low and higher AC current calibration.A LabVIEW program is designed for this accurate automatic calibration to overcome the problems of the manual calibration on the thermal converters.展开更多
An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistiv...An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt rate is outputted. A simplified calculation for the shunt rate was presented based on the feature of the constant-current resistance spot welding and the variation of the resistance in resistance spot welding process, and then the data generated by simplified calculation were used to train and adjust the neural network model. The neural network model proposed was used to predict the shunt rate in the spot welding of 20# mlid steel （in Chinese classification） （in 2. 0 mm thickness） and 10# mild steel （in 1.5 mm and 1.0 mm thickness）. The maximum relative prediction errors are, respectively, 2. 83%, 1.77% and 3.67%. Shunt current compensation experiments were peoCormed based on the neural network prediction model proposed to check the diameter difference of nuggets. Experimental results show that maximum nugget diameter deviation is less than 4% for both 10# and 20# mlid steels with spot spacing of 30 mm and 50 mm.展开更多
文摘AC currents are automatically calibrated by two different thermal current converter(TCC)designs.The two designs are different in the used number of the thermal-elements(TEs).Consequently they differ in their output electro motive force(EMF).Studying the effect of changing the output EMF is done in this paper through calibrating AC currents.5 mA and 5 A are accurately calibrated at different frequencies 55 Hz,1 kHz and10 kHz by the two TCCs.A comparison is made between the results to evaluate the effect of the output EMF value on the accuracy and the uncertainty of the low and higher AC current calibration.A LabVIEW program is designed for this accurate automatic calibration to overcome the problems of the manual calibration on the thermal converters.
基金Acknowledgements The authors would like to thank for the financial support from the National Natural Science Foundation of China through document 51275418. The authors would also like to acknowledge professor Yang Siqian for providing discussion of the results for this study.
文摘An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt rate is outputted. A simplified calculation for the shunt rate was presented based on the feature of the constant-current resistance spot welding and the variation of the resistance in resistance spot welding process, and then the data generated by simplified calculation were used to train and adjust the neural network model. The neural network model proposed was used to predict the shunt rate in the spot welding of 20# mlid steel （in Chinese classification） （in 2. 0 mm thickness） and 10# mild steel （in 1.5 mm and 1.0 mm thickness）. The maximum relative prediction errors are, respectively, 2. 83%, 1.77% and 3.67%. Shunt current compensation experiments were peoCormed based on the neural network prediction model proposed to check the diameter difference of nuggets. Experimental results show that maximum nugget diameter deviation is less than 4% for both 10# and 20# mlid steels with spot spacing of 30 mm and 50 mm.
