This paper has simulated the driving force of solidification crack of stainless steels, that is, stress/strain field in the trail of molten pool. Firstly, the effect of the deformation in the molten pool was eliminate...This paper has simulated the driving force of solidification crack of stainless steels, that is, stress/strain field in the trail of molten pool. Firstly, the effect of the deformation in the molten pool was eliminated after the element rebirth method was adopted. Secondly, the influence of solidification shrinkage was taken into account by increasing thermal expansion coefficients of the steels at elevated temperatures. Finally, the stress/strain distributions of different conditions have been computed and analyzed. Furthermore, the driving force curves of the solidification crack of the steels have been obtained by converting strain time curves into strain temperature curves, which founds a basis for predicting welding solidification crack.展开更多
This paper analyzed the characteristics of welding solidification crack of stainless steels,and clearly re- vealed the the of the deformation in the molten - the pool and the solidification shrinkage on the stress -...This paper analyzed the characteristics of welding solidification crack of stainless steels,and clearly re- vealed the the of the deformation in the molten - the pool and the solidification shrinkage on the stress - strain fields in the trail of molten - weld pool.Moreover, rheologic properties of the alloys in solid - liquid zone were also obtained by measuring the hading and unloading deform curves of the steels.As a result, a numerical model for simulation of stress - strain distributions of welding solidifi- cation crack was developed. On the basis of the model,the thesis simulated the driving force of solidifi- cation crack of stainless steels, that is, stress - strain fields in the trail of molten-weld pool with fi- nite element method.展开更多
This study explored a strategy for predicting the proportion of martensite–austenite(M–A)constituents and impact toughness of stir zone(SZ)on X80 pipeline steel joints welded by friction stir welding(FSW).It is foun...This study explored a strategy for predicting the proportion of martensite–austenite(M–A)constituents and impact toughness of stir zone(SZ)on X80 pipeline steel joints welded by friction stir welding(FSW).It is found that the welding forces,including the traverse force(F_(x)),the lateral force(F_(y))and the plunge force(F_(z)),are the key variables related to the change of welding parameters and influence remarkably the characteristics of M–A constituents and impact toughness of SZ.The impact toughness of SZ is commonly lower than that of the base material due to the formation of lath bainite and coarsening of austenite.The characteristics of M–A constituents in SZ are sensitive to the variation of welding parameters and respond well to the change of welding forces.The proportion of small island M–A constituents increases with the decrease in rotational speed and the increase in Fz.The increase in the amount of island M–A constituents is beneficial to improve the impact toughness of SZ.Based on the above findings,a machine learning(ML)model for predicting the M–A constituents and impact toughness is constructed using the force features as the input data set.The force data-driven ML model can predict the M–A constituents and impact toughness precisely and exhibits higher accuracy than ML built with welding parameters.It is believed that the high accuracy is achieved because the force features include more details of FSW process,such as the heat generation,material flow,plastic deformation,and so on,which govern the microstructural evolution of SZ during FSW.展开更多
文摘This paper has simulated the driving force of solidification crack of stainless steels, that is, stress/strain field in the trail of molten pool. Firstly, the effect of the deformation in the molten pool was eliminated after the element rebirth method was adopted. Secondly, the influence of solidification shrinkage was taken into account by increasing thermal expansion coefficients of the steels at elevated temperatures. Finally, the stress/strain distributions of different conditions have been computed and analyzed. Furthermore, the driving force curves of the solidification crack of the steels have been obtained by converting strain time curves into strain temperature curves, which founds a basis for predicting welding solidification crack.
文摘This paper analyzed the characteristics of welding solidification crack of stainless steels,and clearly re- vealed the the of the deformation in the molten - the pool and the solidification shrinkage on the stress - strain fields in the trail of molten - weld pool.Moreover, rheologic properties of the alloys in solid - liquid zone were also obtained by measuring the hading and unloading deform curves of the steels.As a result, a numerical model for simulation of stress - strain distributions of welding solidifi- cation crack was developed. On the basis of the model,the thesis simulated the driving force of solidifi- cation crack of stainless steels, that is, stress - strain fields in the trail of molten-weld pool with fi- nite element method.
基金financially supported by the National Natural Science Foundation of China(No.52034004).
文摘This study explored a strategy for predicting the proportion of martensite–austenite(M–A)constituents and impact toughness of stir zone(SZ)on X80 pipeline steel joints welded by friction stir welding(FSW).It is found that the welding forces,including the traverse force(F_(x)),the lateral force(F_(y))and the plunge force(F_(z)),are the key variables related to the change of welding parameters and influence remarkably the characteristics of M–A constituents and impact toughness of SZ.The impact toughness of SZ is commonly lower than that of the base material due to the formation of lath bainite and coarsening of austenite.The characteristics of M–A constituents in SZ are sensitive to the variation of welding parameters and respond well to the change of welding forces.The proportion of small island M–A constituents increases with the decrease in rotational speed and the increase in Fz.The increase in the amount of island M–A constituents is beneficial to improve the impact toughness of SZ.Based on the above findings,a machine learning(ML)model for predicting the M–A constituents and impact toughness is constructed using the force features as the input data set.The force data-driven ML model can predict the M–A constituents and impact toughness precisely and exhibits higher accuracy than ML built with welding parameters.It is believed that the high accuracy is achieved because the force features include more details of FSW process,such as the heat generation,material flow,plastic deformation,and so on,which govern the microstructural evolution of SZ during FSW.
