Prediction of Spring-back in Warm and Hot Forming of High Strength Steels by Back Propagation Neural Network Model

Abstract

This research aimed to study the spring-back behavior in warm and hot bending process. From the studied parameters, such as punch radius, the materials are JIS G3135: SPFC440, SPFC590 and SPFC780 high strength steels 1.0 mm. of thickness. Spring-back angles after bending process are measured and calculated to spring-back factor (KR) . The study proposed the model to predict the spring-back values in U-bending by back propagation neural network model. The results showed that the effect of temperature increase can make the spring-back decrease and spring-back angle will increase as the material which has higher tensile strength and the increasing punch radius will increase the spring-back angle and the spring-back factor value of the calculation will decrease when the spring-back angle increase. The result from using artificial neural network to predict spring-back that can be predicted accurately