Dynamic Evaluation Method of Straightness Considering Time-Dependent Springback in Bending-Straightening Based on GA-BP Neural Network

There is a time-dependent springback phenomenon seen during the process of the bending-straightening of slender shafts, which has a great influence on the evaluation of straightness after straightening, creating a risk of misjudgment. This paper presents a dynamic evaluation method of straightness considering time-dependent springback in the bending-straightening process. Firstly, based on viscoelastic mechanics and bending-straightening, the influencing factors of time-dependent springback were analyzed on the basis of certain assumptions, including straightening stroke.delta C, fulcrum distance L, instantaneous springback delta b, straightening time ts, and straightening force Fmax. As the main part of the proposed dynamic evaluation method, the GA-BP neural network is used to establish a model for fast prediction of time-dependent springback in straightening, and it is compared with the linear regression model. The maximum prediction error of the GA-BP model was 0.0038 mm, which was much lower than that of the regression model, at 0.014 mm. The root mean square error (RMSE) of the GA-BP model was 0.0042, and that of the regression model was 0.0098. Finally, the effectiveness of the dynamic straightness evaluation method considering time-dependent springback is verified by experiments. Finally, the sensitivity and relative importance of the influencing factors are analyzed, and the order is delta(C)>t(s)>F-max>L>delta(b).