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- W2349649328 abstract "In this paper,in order to achieve the quality of flash welded joints rail-line testing requirements,the author introduces through low-speed acquisition,the welding process parameters(welding current,welding voltage,etc.) were collected,extracting the characteristics of impact welded joint quality as the input of the RBF neural network,and the characteristics of grey-spots as the output of the RBF neural network,establishes the RBF Neural Network model of welding quality forecasting,and use training models to predict,according to the Ministry of Railways TB/T1632-2005 general specifiction for welding of rails is forecast to reach 80% accuracy rate,as a result of low-speed acquisition,reducing the amount of data and processing capacity.The prediction model can satisfy the real time requirement of rail welding,and to explore the quality of the welded joint forecast of trends." @default.
- W2349649328 created "2016-06-24" @default.
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- W2349649328 date "2010-01-01" @default.
- W2349649328 modified "2023-09-24" @default.
- W2349649328 title "Research on quality forecasting for the flash welded joint rail based on low-frequency data collection" @default.
- W2349649328 hasPublicationYear "2010" @default.
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