The weld pool shape control by intelligent strategy was studied. In order to improve the ability of self-learning and self-adaptation of the ordinary fuzzy control, a self-learning fuzzy neural network controller (FNN...The weld pool shape control by intelligent strategy was studied. In order to improve the ability of self-learning and self-adaptation of the ordinary fuzzy control, a self-learning fuzzy neural network controller (FNNC) for backside width of weld pool in pulsed gas tungsten arc welding (GTAW) with wire filler was designed. In FNNC, the fuzzy system was expressed by an equivalence neural network, the membership functions and inference rulers were decided through the learning of the neural network. Then, the FNNC control arithmetic was analyzed, simulating experiment was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were implemented. The maximum error between the real value and the given one was 0.39mm, the mean error was 0.014mm, and the root-mean-square was 0.14mm. The real backside width was maintained around the given value. The results show that the self-learning fuzzy neural network control strategy can achieve a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.展开更多
This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed, with IM para...This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed, with IM parameters employed as associated elements. The BP algorithm is used to provide an adaptive estimation of the motor speed. The effectiveness of the proposed method is verified by simulation results. The implementation on TMS320F240 fixed DSP is provided.展开更多
Realizing of weld penetration control in gas tungsten arc welding requires establishment of a model describing the relationship between the front-side geometrical parameters of weld pool and the back-side weld width w...Realizing of weld penetration control in gas tungsten arc welding requires establishment of a model describing the relationship between the front-side geometrical parameters of weld pool and the back-side weld width with sufficient accuracy. A neural network model is developed to attain this aim. Welding experiments are conducted to obtain the training data set （including 973 groups of geometrical parameters of the weld pool and back-side weld width） and the verifying data set （108 groups）. Two data sets are used for training and verifying the neural network, respectively. The testing results show that the model has sufficient accuracy and can meet the requirements of weld penetration control.展开更多
文摘The weld pool shape control by intelligent strategy was studied. In order to improve the ability of self-learning and self-adaptation of the ordinary fuzzy control, a self-learning fuzzy neural network controller (FNNC) for backside width of weld pool in pulsed gas tungsten arc welding (GTAW) with wire filler was designed. In FNNC, the fuzzy system was expressed by an equivalence neural network, the membership functions and inference rulers were decided through the learning of the neural network. Then, the FNNC control arithmetic was analyzed, simulating experiment was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were implemented. The maximum error between the real value and the given one was 0.39mm, the mean error was 0.014mm, and the root-mean-square was 0.14mm. The real backside width was maintained around the given value. The results show that the self-learning fuzzy neural network control strategy can achieve a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.
基金This project was supported by the National Natural Science Foundation of China (No. 69874086).
文摘This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed, with IM parameters employed as associated elements. The BP algorithm is used to provide an adaptive estimation of the motor speed. The effectiveness of the proposed method is verified by simulation results. The implementation on TMS320F240 fixed DSP is provided.
基金the Shandong Provincial Natural Science Foundation of China （No. Z2003F05 ）.
文摘Realizing of weld penetration control in gas tungsten arc welding requires establishment of a model describing the relationship between the front-side geometrical parameters of weld pool and the back-side weld width with sufficient accuracy. A neural network model is developed to attain this aim. Welding experiments are conducted to obtain the training data set （including 973 groups of geometrical parameters of the weld pool and back-side weld width） and the verifying data set （108 groups）. Two data sets are used for training and verifying the neural network, respectively. The testing results show that the model has sufficient accuracy and can meet the requirements of weld penetration control.
