2124 Aluminum Alloy Aging Forming Springback Study Based on a Combination Forecasting Method
Zhong Gan, Zhi Wei Qian, Yu Shan Xia · Applied Mechanics and Materials · 2014
This paper proposes a more accurate springback prediction method of ageing forming for 2124 aluminum alloy. In age forming of panels, pre-bending radius, aging time and wall thickness of panels are selected as three parameters, make use of uniform design to arrange experiment and obtain springback radius using ABAQUS simulation. By means of regression analysis, the data is processed to get the influence caused by parameters on springback radius. Regression and BP neural network forecasting method are used respectively to predict springback radius and maximum prediction error is less than 31%. Combination method based on BP neural network is adopted and this method gets the satisfying prediction results that prediction error is within 5%. So conclusion can be drawn that prediction accuracy of combination method is much better than that of regression and BP neural network forecasting.