Optimization of thermal stress and deformation of the casting during solidification by neural network and genetic algorithm

An artificial neural network is combined with the genetic algorithm. Based on some specimens given by FEM, a non-linear mapping function between multiple design variables and multiple control objects is constructed with BP neural networks (NN) in order to obtain the approximate objective function values that are necessary in optimum design using genetic algorithms (GA). An example of a frame-shape specimen (Al-4.5%Cu) is provided in the present work. By analyzing the deformation and thermal stress of the casting, an optimization process is performed for six design parameters including the height of the specimen, the width and the area ratio of the two stress bars, initial temperatures of the casting and the sand mould, and the heat-transfer coefficient as well. Results indicate that an improved solution can be obtained using less finite element analyses. Moreover, the deformation and the thermal stress decrease, respectively, by 58.5% and 40.6% compared with the initial design.