Deformation prediction of thin-walled parts based on BP neural network

In aviation, aerospace and military products, thin-walled parts are widely used for their excellent characteristics. Some key complex parts include thin-walled and special-shaped features, which require high precision. However, due to the low stiffness of thin-walled parts, it is easy to produce machining deformation due to cutting force during processing. Aimed at the difficulty of measuring parts milling deformation, this paper proposes a thin-walled parts processing deformation prediction method based on neural network, designed by the method of orthogonal test, the test program for different milling parameters under the condition of the milling test, test data as the training sample is established based on BP neural network and milling parameters of milling deformation forecast model. Finally, genetic algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the disadvantages of slow convergence rate and easy to fall into local minimum value .The performance of the neural network model is improved.