This article aims to design a dual intelligent control system for straightening and side straightening of precision machine tool guides, in order to improve straightening accuracy and efficiency, and meet the high-precision requirements of precision machine tool guides. Firstly, based on the theory of elastic-plastic deformation, experimental research, and finite element simulation, the bending and rebound characteristics of precision machine tool guide rails are studied, and a mathematical analytical model of the relationship between guide rail rebound curvature and residual curvature is established. Secondly, using a deep neural network (DNN) algorithm model, a prediction model for the rebound amount of precision machine tool guide rails is established based on experimental data, and the relationship between loading stroke and rebound deflection is studied. Combined with the geometric motion model of precision machine tool guide rails, the influence of material characteristic parameters on rebound amount is studied. Finally, a precision machine tool guide rail straightening and side straightening automatic control system based on DNN algorithm was designed, and the feasibility of the model was verified through experiments. The experimental results show that when the lateral bending deformation of the precision machine tool guide rail is small, the straightening stroke calculated by the theoretical model is in good agreement with the finite element simulation and experimental results, with a maximum relative error within 10%. However, when the lateral bending deformation of the guide rail is large, there is a certain gap between the theoretical results and the finite element simulation and experimental results, and the relative error increases. The precision machine tool guide rail straightening and side straightening automatic control system based on DNN algorithm proposed in this article has good straightening effect when the lateral bending deformation of the guide rail is small.
