Plastic Neural Networks
Magnesium alloys, as light structural materials, have many special advantages, such as low density, high specific strength, high specific stiffness, good casting capability, electromagnetic shielding performance and easy reclamation etc. They are extensively used in automobiles, airplanes, computers and communication equipments etc. therefore, Magnesium alloys are considered as the materials having development potential and best future in 21st century.Because of the excellent performance about Magnesium alloy sheet plastic forming products. the magnesium alloy sheet metal forming processes.was received much attention.First of all, This thesis is described about ME20M magnesium alloy sheet, the one-way mechanical tensile test with the different temperature and the speed of forming, The relevant data and curves is attained; Meanwhile, Studies on the relationship between plastic forming performance, forming temperature and tensile speed. Use of numerical simulation technology research on technological parameters magnesium alloy sheet of drawing, and compared with the experimental results, with a view to guide the numerical simulation of the actual processing.Then, on AZ31 magnesium alloy sheet metal forming process of drawing, the friction coefficient test, can draw the laws of friction coefficient variation with the two technical parameters of the temperature, BHF.The BP neural network is better than the traditional mathematics module in nonlinear system prediction. At last, to meet the demand of improving accuracy, the paper is designed the flow stress prediction model based on BP neural networks, which applied the neural networks to practical engineering in magnesium alloy ME20M. It successfully forecasts flow stress, The forecasted value can satisfy the demand of the projects. It is meaningful for the practical application.
Source: Engineering Science Paper
Field Theoretical Tools for Polymer and Particle Physics (Lecture Notes in Physics)
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