The joining of a 6-mm thickness Al 6061 to Stainless steel 304 has been performed by solid state welding. A selection method of optimum friction welding condition using neural networks is proposed. The data used for a...The joining of a 6-mm thickness Al 6061 to Stainless steel 304 has been performed by solid state welding. A selection method of optimum friction welding condition using neural networks is proposed. The data used for analyses are the friction stir welding condition, the input parameters of the model consist of welding speed and tool rotation speed. The outputs of the ANN (Artificial Neural Network)model includes resulting parameters, namely, maximum reached temperature,and heating rate for both aluminum alloy 6061 and stainless steel 304 during friction stir welding process.The results of analysis suggest that the proposed method is an effective one to select an optimum welding condition.Good performance of the ANN model was achieved. The combined influence of welding speed and tool rotation speed on the maximum reached temperature and heating rate for both aluminum alloy 6061and stainless steel 304 friction stir welding was simulated. A comparison was made between the output of the ANN program and finite element model. The calculated results were in good agreement with that of finite element model.展开更多
In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule sampl...In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.展开更多
文摘The joining of a 6-mm thickness Al 6061 to Stainless steel 304 has been performed by solid state welding. A selection method of optimum friction welding condition using neural networks is proposed. The data used for analyses are the friction stir welding condition, the input parameters of the model consist of welding speed and tool rotation speed. The outputs of the ANN (Artificial Neural Network)model includes resulting parameters, namely, maximum reached temperature,and heating rate for both aluminum alloy 6061 and stainless steel 304 during friction stir welding process.The results of analysis suggest that the proposed method is an effective one to select an optimum welding condition.Good performance of the ANN model was achieved. The combined influence of welding speed and tool rotation speed on the maximum reached temperature and heating rate for both aluminum alloy 6061and stainless steel 304 friction stir welding was simulated. A comparison was made between the output of the ANN program and finite element model. The calculated results were in good agreement with that of finite element model.
文摘In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.
