Experimental and Numerical Simulation Studies on V-Shaped Bending of Aluminum/CFRP Laminates.

With the increasing requirements of automotive lightweighting, metal/CFRP laminates are increasingly used. In this paper, Al/CFRP laminates were prepared using an integrated hot press curing method, and the optimum curing conditions were determined using the single-lap shear test at 130 °C for 45 min. The effects of fiber lay-up, forming speed, and metal layer thickness on bending springback were investigated using the V-shaped bending test and Abaqus finite element analysis method. The results show that fiber lay-up has an important influence on springback. Among the five different fiber lay-ups (0° unidirectional, 90° unidirectional, 0° orthotropic, 90° orthotropic, and 45° orthotropic), the 45° orthotropic lay-up had the lowest springback rate of 1.11%. Increasing the thickness of the sheet metal can significantly reduce the resilience rate. As the sheet thickness increased from 2 mm to 3 mm, the springback of the 90° unidirectional lay-up decreased by 43%. Springback was not sensitive to forming speed, and the difference in springback was within 1% at different forming speeds. The damage behavior of the forming process was analyzed using the three-dimensional Hashin damage law with the Vumat subroutine and microscopic analysis. Fiber and resin damage under 45° orthotropic lay-up conditions was relatively small compared to fiber damage under 0° unidirectional lay-up and resin damage under 90° unidirectional lay-up.

A Versatile Punch Stroke Correction Model for Trial V-Bending of Sheet Metals Based on Data-Driven Method.

During air bending of sheet metals, the correction of punch stroke for springback control is always implemented through repeated trial bending until achieving the forming accuracy of bending parts. In this study, a modelling method for correction of punch stroke is presented for guiding trial bending based on a data-driven technique. Firstly, the big data for the model are mainly generated from a large number of finite element simulations, considering many variables, e.g., material parameters, dimensions of V-dies and blanks, and processing parameters. Based on the big data, two punch stroke correction models are developed via neural network and dimensional analysis, respectively. The analytic comparison shows that the neural network model is more suitable for guiding trial bending of sheet metals than the dimensional analysis model, which has mechanical significance. The actual trial bending tests prove that the neural-network-based punch stroke correction model presents great versatility and accuracy in the guidance of trial bending, leading to a reduction in the number of trial bends and an improvement in the production efficiency of air bending.