Article
A Versatile Punch Stroke Correction Model for Trial V-Bending
of Sheet Metals Based on Data-Driven Method
Yongsen Yu
1,2
, Zhiping Guan
1,2,
* , Mingwen Ren
1,2
, Jiawang Song
1,2
, Pinkui Ma
1,2
and Hongjie Jia
1,2
Citation: Yu, Y.; Guan, Z.; Ren, M.;
Song, J.; Ma, P.; Jia, H. A Versatile
Punch Stroke Correction Model for
Trial V-Bending of Sheet Metals Based
on Data-Driven Method. Materials
2021, 14, 4790. https://doi.org/
10.3390/ma14174790
Academic Editor: Arkadiusz Gola
Received: 29 July 2021
Accepted: 18 August 2021
Published: 24 August 2021
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4.0/).
1
Key Laboratory of Automobile Materials of Ministry of Education & School of Materials Science and
Engineering, Jilin University, 5988 Renmin Street, Changchun 130022, China; yuys19@mails.jlu.edu.cn (Y.Y.);
renmw@jlu.edu.cn (M.R.); songjw@jlu.edu.cn (J.S.); mapk@jlu.edu.cn (P.M.); jiahj@jlu.edu.cn (H.J.)
2
International Center of Future Science, Jilin University, Changchun 130012, China
* Correspondence: guanzp@jlu.edu.cn
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.
Keywords: V-bending; springback; punch stroke; neural network; dimensional analysis
1. Introduction
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
]. In air bending, therefore, precise bending has to be guided by
a springback prediction model that represents the accurate relationship between punch
stroke and forming angle [
3
]. However, the factors for springback of sheet metals in air
bending are so complicated that the springback prediction models have a certain degree of
error no matter how accurate they are [
4
]. In the air bending process, the forming angles
need to gradually approach the expected value via repeating trial bending; that is, the
punch stroke keeps being corrected until the tolerance of the forming angle is reached.
The punch stroke correction model, which affords a relationship between deviation of
the bending angle and correction of punch stroke, is also critical for sheet bending and has
been paid much less attention than the springback prediction models [5,6]. The deviation
of the bending angle and the correction of punch stroke should be the differential or
variational perturbation of the bending angle and punch stroke, respectively. Consequently,
the punch stroke correction model could be regarded as the differential or variational
form of the springback prediction model. If the springback prediction model presents
an explicit analytical formula, the punch stroke correction model can be obtained by
differentiation calculation. Some springback prediction models have been analytically
deduced by means of mechanical analysis, considering the geometrical dimensions of
Materials 2021, 14, 4790. https://doi.org/10.3390/ma14174790 https://www.mdpi.com/journal/materials
