Mechanism and control of nonuniform phase transformation of microalloyed dual-phase steel during cooling process after hot rolling

After cooling in the hot rolling process,the metallographic structure of microalloyed dual-phase steel is nonuniform along the rolling direction,while the thickness fluctuation of microalloyed dual-phase steel with a nonuniform metallographic structure will occur during cold rolling.The mechanism of nonuniform phase transformation of microalloyed dual-phase steels was studied during the cooling process after hot rolling,and the nonuniform phase transformation of microalloyed dual-phase steel was regulated during the cooling process after hot rolling through process optimization.First,the empirical equation of phase transformation temperature was measured by a dilatometer considering thermal expansion.Then,the phase field and temperature field of laminar cooling process were calculated to provide initial boundary conditions for the finite element model.After that,the coupling finite element model of the temperature phase transformation of the strip steel in coiling transportation process was established.The simulation results show that the different thermal contact conditions of the microalloyed dual-phase steel during coil transportation lead to uneven cooling of the coil,which leads to nonuniform transformation of the coil along the rolling direction.In addition,by prolonging the time interval from coiling to unloading,the phenomenon of nonuniform phase transformation of microalloyed dual-phase steel can be effectively controlled.The simulation results are applied to industrial production.The application results show that prolonging the time interval from coiling to unloading can effectively improve the nonuniform phase transformation of microalloyed dual-phase steel in the cooling process after hot rolling.

Effect of post rolling stress on phase transformation behavior of microalloyed dual phase steel

The slow phase transformation of microalloyed dual phase steel makes the nonuniform stress and temperature fields during the post rolling cooling process have a significant impact on the phase transformation process.Given the relatively slow phase transformation of DP780 steel within the microalloyed dual phase steel series,the influence of stress on the phase transformation behavior of DP780 steel was investigated.To quantify the nonuniform thermal and stress conditions in the steel coil,a thermo-mechanical coupled finite element model of the hot-rolled strip cooling process was established.Based on the simulation data,DP780 steel was chosen as the research material,and Gleeble 3500 thermal simulation equipment was used for experimental validation.The thermal expansion curves were analyzed through regression to establish the dynamic model of DP780 steel phase transformation under stress.Subsequently,metallographic analysis was conducted to determine phase transformation type and grain size of DP780 steel.The results confirmed that the stress promotes the occurrence of semi-diffusion-type bainite transformation.Furthermore,an appropriate level of stress facilitates the growth of bainitic grains,while the increased stress inhibits the growth of ferritic grains.

Thermal-force control ability under variation of electromagnetic stick structure parameters in RPECT

Thermal-force driving of roll profile electromagnetic control technology(RPECT),which can be used to adjust the roll profile,can be affected by the sequential temperature rise between the electromagnetic stick(ES)and electromagnetic control roll.Due to the limited space of ES and induction coil,the cross-sectional area of induction coil can be inevitably affected by changing the size of the ES induction zone,which can further change the energy input under the same electromagnetic parameters,the temperature rising effect and the bulging ability.To investigate this phenomenon,the effects of the radius of the induction zone on the thermal-force contribution ratio,the heating ability of ES and the temperature distribution were analyzed through an electromagnetic-thermal-structural finite element model.To ensure that the results are applicable to RPECT,the thermal energy conversion ability and thermal-force roll crown control ability under different lengths of the induction zone were analyzed.It was found that whether the current density regulation mode or the current frequency regulation mode is adopted,the cases with 20 or 25 mm radius of the induction zone have the great thermal energy conversion ability and the good thermal-force roll crown control ability.The reasonable adjustment of the length of the induction zone can reduce the radius required for the maximum energy efficiency regulation.Combined with the results of the simulation analysis,the optimization of ES based on the control ability maximization requirement is achieved,which provides the base for the design and configuration of ES in RPECT.

A defect recognition model for cross-section profile of hot-rolled strip based on deep learning

The cross-section profile is a key signal for evaluating hot-rolled strip quality,and ignoring its defects can easily lead to a final failure.The characteristics of complex curve,significant irregular fluctuation and imperfect sample data make it a challenge of recognizing cross-section defects,and current industrial judgment methods rely excessively on human decision making.A novel stacked denoising autoencoders(SDAE)model optimized with support vector machine(SVM)theory was proposed for the recognition of cross-section defects.Firstly,interpolation filtering and principal component analysis were employed to linearly reduce the data dimensionality of the profile curve.Secondly,the deep learning algorithm SDAE was used layer by layer for greedy unsupervised feature learning,and its final layer of back-propagation neural network was replaced by SVM for supervised learning of the final features,and the final model SDAE_SVM was obtained by further optimizing the entire network parameters via error back-propagation.Finally,the curve mirroring and combination stitching methods were used as data augmentation for the training set,which dealt with the problem of sample imbalance in the original data set,and the accuracy of cross-section defect prediction was further improved.The approach was applied in a 1780-mm hot rolling line of a steel mill to achieve the automatic diagnosis and classification of defects in cross-section profile of hot-rolled strip,which helps to reduce flatness quality concerns in downstream processes.