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- W2372957729 abstract "Based on in-depth study on longitudinal welded pipe high frequency induction welding mechanism,it determined 3 welding process parameters affecting welding quality greatly,such as max. opening angle,current frequency and the distance from coil to welding V point. Meanwhile,it extracted two forecasts,including temperature difference along steel pipe wall thickness in weld,which characterize welding quality,and the maximum equivalent residual stress in HAZ. Then using genetic algorithm to optimize the BP neural network to establish the nonlinear mapping model between process parameters and forecast target. Test results showed that the prediction accuracy is within ±5%,the BP neural network generalization ability is good,which can be applied to the actual forecast for longitudinal welded pipe high-frequency induction welding quality." @default.
- W2372957729 created "2016-06-24" @default.
- W2372957729 creator A5067579577 @default.
- W2372957729 date "2014-01-01" @default.
- W2372957729 modified "2023-09-25" @default.
- W2372957729 title "Welding Quality Forecast for Longitudinal Welded Pipe High Frequency Induction Welding Based on Neural Network" @default.
- W2372957729 hasPublicationYear "2014" @default.
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