Wen Jing-lin

Continuous extruding extending forming of semi-solid A2017 alloy

Continuous extruding/ extending forming process for A20/7 alloy in semisolid state was proposed through installing extending die at the outlet of shearing-cooling-rolling (SCR) machine. A series of experiments to produce flat bar of A 2017 alloy were carried out. The forming process, metal flow behavior in die and microstructure and mechanical property of products were investigated. It is shown that if the pouring temperature of melt was higher, the die was filled with semi-solid slurry with low solid fraction and periodical cracks would occur on the product surface; If its pouring temperature was lower or the preheating temperature of die was lower, semisolid slurry would solidify rapidly and block the die after entering the cavity. The analysis of mass flow trace shows that the semi-solid slurry moves forward layer by layer and fills the die extending caviiy in radiation manner and the velocity of mass flow in the central area of extending cavity and exit of mould is the maximum, and then decreases gradually from the center to both sides of die wall. By increasing the die extending angle, the velocity of mass flow becomes more homogeneous. Under rational process control and die design, the A2017 flat bar with transverse section of 10 × 50 mm and with good surface and fine equiaxed grains can be obtained by continuous extruding/ extending forming process. The product’s tensile strength and elongation are 420.5 MPa and 14.2%, respectively.

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.

An experiment study on the Castex process for AS wire

In order to optimize the Castex process of AS wire, the systematic experiments have been done for different process parameters with self-made DZJ-II 350 Castex machine. The parameters, such as casting temperature of aluminum, flow of cooling water, extrusion ratio and the gap between the surface of wheel and that of the mould, have been mainly studied. The results show that with the increase of casting temperature or rotating speed of wheel the measured length of liquid metal zone increases too. However, the length of liquid metal zone decreases with the increase of the flow of cooling water. Moreover, the relationship between the extrusion ratio and the extrusion power is studied.