Abstract
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.
Highlights
Sheet metal bending is a representative forming craft in manufacturing industries [1].“Springback” refers to the elastically driven change in shape that occurs following a sheet bending when forming loads are removed from the work piece, which causes problems such as increased tolerance and variability in subsequent forming operations, in assembly, and in the final part(s) [2]
The punch stroke correction model, which affords a relationship between deviation of the bending angle and correction of punch stroke, is critical for sheet bending and has been paid much less attention than the springback prediction models [5,6]
We aimed to propose a punch stroke correction model for trial tests in metal sheet bending, which would be able to guide the accurate correction of punch stroke with high efficiency
Summary
Sheet metal bending is a representative forming craft in manufacturing industries [1].“Springback” refers to the elastically driven change in shape that occurs following a sheet bending when forming loads are removed from the work piece, which causes problems such as increased tolerance and variability in subsequent forming operations, in assembly, and in the final part(s) [2]. Precise bending has to be guided by a springback prediction model that represents the accurate relationship between punch stroke and forming angle [3]. The punch stroke correction model could be regarded as the differential or variational form of the springback prediction model. Some springback prediction models have been analytically deduced by means of mechanical analysis, considering the geometrical dimensions of forming dies and work pieces, the mechanical properties of sheet metals, processing parameters, etc. These analytical models are not accurate enough, due to simplifications and assumptions during mechanical analysis because the influence of springback in sheet bending is highly nonlinear, involving many complicated factors
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