Liu Kefu

Artificial neural network model of abrasive water jet cutting stainless steel process

Abrasive water jet is one of the advanced green machining tools and its advantages are well known. In order to obtain a product with high surface quality, the abrasive water jet machining process must be precisely controlled. Based on the artificial neural network, a model for the abrasive water jet cutting stainless steel process was built. The artificial neural network was then trained based on sample data set using improved BP algorithm. The trained network establishes nonlinear relationships among the parameters of abrasive water jet cutting process and cutting surface quality. Consequently the surface quality of the part can be indirectly controlled by adjusting the cutting speed of water jet. The satisfied results were obtained using the trained artificial neural network model through the check data set.

Analysis of tilting torque of steel ladle based on ANSYS

Steel ladle is the important tool for containing and transiting in steel enterprises. Analysis of tilting torque steel ladle is very important for reasonable design of electric tipping mechanism of steel ladle. Based on the ANSYS, the varies of steel ladle total mass, centroid, moment of inertia, tilting torque etc. with tilting angle in steel ladle tipping process are studied. The tipping torque was fitted based on MATLAB. It was showed that maximum torque is 554254N.M when tipping angle is 50 °. It has great application value in calculates a variety of mechanical properties of tipping as well as design of steel ladle and electric execute mechanism for steel ladle.