Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of flow stress in isothermal compression of Ti–6Al–4V alloy using fuzzy neural network
AbstractIsothermal compression of Ti–6Al–4V alloy at the deformation temperatures ranging from 1093K to 1303K with an interval 20K, the strain rates ranging from 0.001s−1 to 10.0s−1 and the height reductions ranging from 20% to 60% with an interval 10% were carried out on a Thermecmaster-Z simulator. Based on the experimental results, a model for the flow stress in isothermal compression of Ti–6Al–4V alloy was established in terms of the fuzzy neural network (FNN) with a back-propagation learning algorithm using strain, strain rate and deformation temperature as inputs. The maximum difference and the average difference between the predicted and the experimental flow stress are 18.7% and 4.76%, respectively. The comparison between the predicted results based on the FNN model for flow stress and those using the regression method has illustrated that the FNN model is more efficient in predicting the flow stress of Ti–6Al–4V alloy.
Prediction of flow stress in isothermal compression of Ti–6Al–4V alloy using fuzzy neural network
AbstractIsothermal compression of Ti–6Al–4V alloy at the deformation temperatures ranging from 1093K to 1303K with an interval 20K, the strain rates ranging from 0.001s−1 to 10.0s−1 and the height reductions ranging from 20% to 60% with an interval 10% were carried out on a Thermecmaster-Z simulator. Based on the experimental results, a model for the flow stress in isothermal compression of Ti–6Al–4V alloy was established in terms of the fuzzy neural network (FNN) with a back-propagation learning algorithm using strain, strain rate and deformation temperature as inputs. The maximum difference and the average difference between the predicted and the experimental flow stress are 18.7% and 4.76%, respectively. The comparison between the predicted results based on the FNN model for flow stress and those using the regression method has illustrated that the FNN model is more efficient in predicting the flow stress of Ti–6Al–4V alloy.
Prediction of flow stress in isothermal compression of Ti–6Al–4V alloy using fuzzy neural network
Luo, Jiao (Autor:in) / Li, Miaoquan (Autor:in) / Yu, Weixin (Autor:in)
07.01.2010
6 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Prediction of flow stress in isothermal compression of Ti-6Al-4V alloy using fuzzy neural network
| British Library Online Contents | 2010
| British Library Online Contents | 2011
The fuzzy neural network model of flow stress in the isothermal compression of 300M steel
| British Library Online Contents | 2012