The model describing the dependence of the mechanical properties on the chemical composition and as deformation techniques of tungsten heavy alloy is established by the method of improved the backpropagation neural network. The mechanical properties＇ parameters of tungsten alloy and deformation techniques for tungsten alloy are used as the inputs. The chemical composition and deformation amount of tungsten alloy are used as the outputs. Then they are used for training the neural network. At the same time, the optimal number of the hidden neurons is obtained through the experiential equations, and the varied step learning method is adopted to ensure the stability of the training process. According to the requirements for mechanical properties, the chemical composition and the deformation condition for tungsten heavy alloy can be designed by this artificial neural network system.
采用L ap lace变换、Schapery数值反演及有限元分析的方法,在理论计算和实验验证的基础上,提出了由纤维增强树脂基复合材料所组成的润滑系统的有效弹性模量的一种简化计算方法.通过该方法对玻璃纤维增强环氧树脂基复合材料的点接触润滑情况进行了分析计算,得出了润滑系统油膜厚度随时间的变化规律.通过实验验证了该理论计算方法的正确性与可行性,为树脂基复合材料的润滑研究提供了可靠的理论依据和实验资料.
