Qiu Chunlin

MICROSTRUCTURAL CHARACTERS AND TOUGHNESS OF DIFFERENT SUB–REGIONS IN THE WELDING HEAT AFFECTED ZONE OF LOW CARBON BAINITIC STEEL

It is generally recognized that welding heat affected zone(WHAZ) is the poorest toughness region in the welded joint of low carbon bainitic steels.The thermomechanical simulator was employed to simulate the welding thermal cycle processes of different sub-regions in WHAZ of low carbon bainitic steel in this work.The toughness of simulated specimens were tested on the instrumented drop weight impact tester with oscilloscope,and miscrostructure features were observed by means of OM,SEM,TEM and EBSD.The results showed that when cooling time(t_(8/5)) was 30 s,the crack initiation energy of various sub-regions was similar,and the range of their values was between 40 and 70 J.However,fine grained heat affected zone(FGHAZ) exhibited excellent crack arrest properties because the impact load-time curve included wide crack ductile propagation and crack brittle propagation stages.By contrast,the crack propagation energy of intercritical heat affected zone (ICHAZ) and coarse grained heat affected zone(CGHAZ) obviously deteriorated.With the increase in cooling time,both crack initiation energy and crack propagation energy of various sub-regions decreased,in which the crack initiation energy of CGHAZ and the crack propagation energy of FGHAZ decreased notably.Under different cooling rates,the variation of morphology and size of M-A constituents was mainly responsible for the deterioration of crack initiation energy.As for crack propagation energy,the FGHAZ had a good resistance to crack propagation due to high density of high angle grain boundary.Therefore,its crack propagation energy was far superior to other sub-regions. There was uneven effective grain size in the ICHAZ and ferrite grain grew with the decease in cooling rate,which decreased the crack propagation energy.In the CGHAZ,prior austenite grains coarsened and the density of high angle grain boundaries decreased greatly,which resulted in the decrease in crack propagation energy.

Friction coefficient of hot tandem finishing mill predicted by BP neural network

The friction coefficients of hot tandem finishing mill were used to be set as a fixed value, which lead to 2000~3000 kN rolling force deviation in the prediction. During the hot rolling, the friction coefficient is affected by lots of factors and the variation principle is relatively intricate. In this article, the BP neural network consisted of an input layer, an output layer and one or several hidden layers and there were several nodes in each layer. 7 input variables were investigated in the input layer. The output was friction coefficient. the friction coefficient was calculated according to Sims formula. 3090 sets of data from production were surveyed in this paper; the same amount of friction coefficient can also be calculated. 900 sets were used for training the network, after training, the network can obtain high accuracy in prediction. 40% of data were used for testing and the rest for verification. The results displayed the minimum error was only 0.00000193, the correlation coefficient reached 0.9977 and all data were located in 5% deviation. The achievements proved the BP neural network was an effective and reliable method to predict the friction coefficient during hot rolling. Furthermore, the BP neural network can enhance the precision of rolling force prediction considerably. With the BP neural network algorithm, the friction coefficients affected by multiple variables during hot rolling can be predicted correctly, which provides important information for improving the accuracy of rolling pressure prediction.