Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Highlights ► The flow stress of 300M steel decreases with the increasing of the deformation temperature. ► The flow stress of 300M steel increases with the increasing of strain rate. ► A fuzzy neural network model is applied to predict the flow stress of 300M steel. ► The predicted flow stress is in a good agreement with the experimental flow stress of 300M steel.
Abstract The isothermal compression of 300M steel is carried out on a Gleeble-3500 simulator at the deformation temperatures ranging from 1173K to 1413K, the strain rates ranging from 0.1s−1 to 25.0s−1 and a strain of 0.69. The experimental results show that the flow stress decreases with the increasing of deformation temperature, and increases with the increasing of strain rate. The fuzzy neural network method with a back-propagation learning algorithm and the regression method are adopted to model the flow stress in the isothermal compression of 300M steel respectively. All of the results have sufficiently indicated that the predicted accuracy of flow stress in the isothermal compression of 300M steel by using fuzzy neural network model is better that using the regression model, and the present approach is effective to predict the flow stress in the isothermal compression of 300M steel.
Highlights ► The flow stress of 300M steel decreases with the increasing of the deformation temperature. ► The flow stress of 300M steel increases with the increasing of strain rate. ► A fuzzy neural network model is applied to predict the flow stress of 300M steel. ► The predicted flow stress is in a good agreement with the experimental flow stress of 300M steel.
Abstract The isothermal compression of 300M steel is carried out on a Gleeble-3500 simulator at the deformation temperatures ranging from 1173K to 1413K, the strain rates ranging from 0.1s−1 to 25.0s−1 and a strain of 0.69. The experimental results show that the flow stress decreases with the increasing of deformation temperature, and increases with the increasing of strain rate. The fuzzy neural network method with a back-propagation learning algorithm and the regression method are adopted to model the flow stress in the isothermal compression of 300M steel respectively. All of the results have sufficiently indicated that the predicted accuracy of flow stress in the isothermal compression of 300M steel by using fuzzy neural network model is better that using the regression model, and the present approach is effective to predict the flow stress in the isothermal compression of 300M steel.
The fuzzy neural network model of flow stress in the isothermal compression of 300M steel
23.04.2012
6 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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