As the base of the research work on the weld shape control during pulsed gas tungsten arc welding （GTAW） with wire filler, this paper addressed the modeling of the dynamic welding process. Topside length Lt, maximum...As the base of the research work on the weld shape control during pulsed gas tungsten arc welding （GTAW） with wire filler, this paper addressed the modeling of the dynamic welding process. Topside length Lt, maximum width Wt and half-length ratio Rh1 were selected to depict topside weld pool shape, and were measured on-line by vision sensing. A dynamic neural network model was constructed to predict the usually unmeasured backside width and topside height of the weld through topside shape parameters and welding parameters. The inputs of the model were the welding parameters （peak current, pulse duty ratio, welding speed, filler rate）, the joint gap, the topside pool shape parameters （Lt, Wt, and Rh1）, and their history values at two former pulse, a total of 24 numbers. The validating experiment results proved that the artificial neural network （ANN） model had high precision and could be used in process control. At last, with the developed dynamic model, steady and dynamic behavior was analyzed by simulation experiments, which discovered the variation rules of weld pool shape parameters under different welding parameters, and further knew well the characteristic of the welding process.展开更多
Artificial neural networks have been studied for applicability for modeling of spot welding process. Some basic concepts relating to neural networks are explained as well as how they can be used to model welding quali...Artificial neural networks have been studied for applicability for modeling of spot welding process. Some basic concepts relating to neural networks are explained as well as how they can be used to model welding qualitv parameters in terms of the welding process parameter. The performance of the neural networks for modeling is presented and evaluated using actual welding data. It is concluded that neural network modeling is a good means of estimating spot welding quality on-line.展开更多
Linear friction welding （LFW） is a solid state process for joining metals together. While this process was developed originaUy for titanium alloys （e. g. blisks ） , over the past decade a number of materials were ...Linear friction welding （LFW） is a solid state process for joining metals together. While this process was developed originaUy for titanium alloys （e. g. blisks ） , over the past decade a number of materials were found to be weldable with LFW. In this review, the current status of understanding and development of LFW are presented. Particular emphasis has been given to the modeling of the LFW process. Finally, opportunities for further research and development of LFW are identified.展开更多
Modeling is essential, significant and difficult for the quality and shaping control of arc welding process. A generalized rough set based modeling method was brought forward and a dynamic predictive model for pulsed ...Modeling is essential, significant and difficult for the quality and shaping control of arc welding process. A generalized rough set based modeling method was brought forward and a dynamic predictive model for pulsed gas tungsten arc welding (GTAW) was obtained by this modeling method. The results show that this modeling method can well acquire knowledge in welding and satisfy the real life application. In addition, the results of comparison between classic rough set model and back-propagation neural network model respectively are also satisfying.展开更多
基金This work was supported by the National Natural Sci-ence Foundation of China(Grant No.59635160)the Weapon Pre-Research Foundation of China(Grant No.51418050404HT0159).
文摘As the base of the research work on the weld shape control during pulsed gas tungsten arc welding （GTAW） with wire filler, this paper addressed the modeling of the dynamic welding process. Topside length Lt, maximum width Wt and half-length ratio Rh1 were selected to depict topside weld pool shape, and were measured on-line by vision sensing. A dynamic neural network model was constructed to predict the usually unmeasured backside width and topside height of the weld through topside shape parameters and welding parameters. The inputs of the model were the welding parameters （peak current, pulse duty ratio, welding speed, filler rate）, the joint gap, the topside pool shape parameters （Lt, Wt, and Rh1）, and their history values at two former pulse, a total of 24 numbers. The validating experiment results proved that the artificial neural network （ANN） model had high precision and could be used in process control. At last, with the developed dynamic model, steady and dynamic behavior was analyzed by simulation experiments, which discovered the variation rules of weld pool shape parameters under different welding parameters, and further knew well the characteristic of the welding process.
文摘Artificial neural networks have been studied for applicability for modeling of spot welding process. Some basic concepts relating to neural networks are explained as well as how they can be used to model welding qualitv parameters in terms of the welding process parameter. The performance of the neural networks for modeling is presented and evaluated using actual welding data. It is concluded that neural network modeling is a good means of estimating spot welding quality on-line.
基金The work is supported by the National Natural Science Foundation of China （51005180）, the Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China （ 131052 ） , the Fundamental Research Funds for the Central Universities （3102014JC02010404）, the Fundamental Research Fund of NPU （JC201233）, and the 111 Project of China （B08040）.
文摘Linear friction welding （LFW） is a solid state process for joining metals together. While this process was developed originaUy for titanium alloys （e. g. blisks ） , over the past decade a number of materials were found to be weldable with LFW. In this review, the current status of understanding and development of LFW are presented. Particular emphasis has been given to the modeling of the LFW process. Finally, opportunities for further research and development of LFW are identified.
基金The National Natural Science Foundation of China(No 60474036)
文摘Modeling is essential, significant and difficult for the quality and shaping control of arc welding process. A generalized rough set based modeling method was brought forward and a dynamic predictive model for pulsed gas tungsten arc welding (GTAW) was obtained by this modeling method. The results show that this modeling method can well acquire knowledge in welding and satisfy the real life application. In addition, the results of comparison between classic rough set model and back-propagation neural network model respectively are also satisfying.
