Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

References

References
1.
Yang
,
M. Y.
,
Sun
,
X. P.
, and
Hu
,
Z. Y.
,
2006
, “
The Study of Influencing Factors and Numerical Simulation of Forming Process of Covering Parts in Automotive
,”
Die and Mould Technology
,
2006
(1), pp.
3
7
(in Chinese).
2.
Lai
,
X. W.
,
2006
, “
The Study of Springback Prediction in Sheet Metal Stamping Based on Combined Neural Network Prediction
,” Dissertation, Zhejiang University, Hangzhou, China (in Chinese).
3.
Ghouati
,
O.
,
Joannic
,
D.
, and
Gelin
,
J. C.
,
1998
, “
Optimization of Process Parameters for the Control of Springback in Deep Drawing
,” NUMIFORM’98, Enschede, The Netherlands, pp.
819
824
.
4.
Yan
,
S. L.
,
Hu
,
S. G.
,
Xu
,
H.
, and
Li
,
G. S.
,
2008
, “
The Generalized Physical Model of Regenerative System Thermal Power Unit and Its Soda Distribution Equation Solution
,”
Journal Of Chinese Society Of Power Engineering
,
28
(3), pp.
480
482
(in Chinese).
5.
Wang
,
L.
,
2009
, “
The Study of Remote Sensing Monitor of Larch Forest Pests Based on the Physical Model
,” Dissertation, Beijing Agricultural University, Beijing, China (in Chinese).
6.
Li
,
Y. K.
,
2012
, “
The Researching Analysis and Improving Application of BP Neural Network
,” Dissertation, Anhui University of Science and Technology, Huainan, China (in Chinese).
7.
Daehn
,
G. S.
,
Vohnout
,
V. J.
, and
Datta
,
S.
,
2000
, “
Hyperplastic Forming: Process Potential and Factors Affecting Formability
,”
1999 MRS Fall Meeting
, Vol.
601
, pp.
247
252
.
8.
Xing
,
G. J.
, and
Ha
,
M. H.
,
2013
,
Feedforward Neural Network and Its Application
,
Science Press
,
Beijing
(in Chinese).
9.
Cho
,
J. R.
,
Moon
,
S. J.
,
Moon
,
Y. H.
, and
Kang
,
S. S.
,
2003
, “
Finite Element Investigation on Springback Characteristics in Sheet Metal U-Bending Process
,”
J. Mater. Process. Technol.
,
141
(
1
), pp.
109
116
.
10.
Wang
,
C.
,
Kinzel
,
G.
, and
Altan
,
T.
,
1993
, “
Process Simulation and Springback Control Inplane–Strain Sheet Bending
,” SAE Paper No. 930280.
11.
Rees
,
D. W. A.
,
2001
, “
Factors Influencing the FLD of Automotive Sheet Metal
,”
J. Mater. Process. Technol.
,
118
(1–3), pp.
1
8
.
12.
Jiang
,
Y. M.
,
2012
, “
The Study of the Data Acquisition and Analysis System Based on MATLAB and Its Design
,” Dissertation, Shandong University, Shandong, China (in Chinese).
13.
Fu
,
H. X.
, and
Zhao
,
H.
,
2010
,
The Application Design of Neural Network in MATLAB
,
Mechanical Industry Press
,
Beijing, China
(in Chinese).
14.
Sun
,
P.
,
Gracio
,
J. J.
, and
Ferreira
,
J. A.
,
2006
, “
Control System of a Mini Hydraulic Press for Evaluating Springback in Sheet Metal Forming
,”
J. Mater. Process. Technol.
,
176
(1–3), pp.
55
57
.
15.
Xu
,
Z.
,
2010
, “
Drawing of 5A06 Aluminum Alloy Cylindrical Cup With Double Side Pressure
,” Dissertation, Harbin Industrial University, Harbin, China (in Chinese).
16.
Li
,
Y.
,
Cao
,
H.
,
Niu
,
L.
, and
Jin
,
X.
,
2015
, “
A General Method for the Dynamic Modeling of Ball Bearing–Rotor Systems
,”
ASME J. Manuf. Sci. Eng.
,
137
(
2
), p.
021016
.
17.
Li
,
S.
,
Du
,
S.
,
Tang
,
A.
,
Landers
,
R. G.
, and
Zhang
,
Y.
,
2015
, “
Force Modeling and Control of SiC Monocrystal Wafer Processing
,”
ASME J. Manuf. Sci. Eng.
,
137
(
6
), p.
061003
.
18.
Cho
,
J. H.
, and
Hwang
,
S. M.
,
2013
, “
A New Model for the Prediction of Roll Deformation in a 20-High Sendzimir Mill
,”
ASME J. Manuf. Sci. Eng.
,
136
(
1
), p.
011004
.
19.
Ghavam
,
K.
,
Bagheriasl
,
R.
, and
Worswick
,
M. J.
,
2013
, “
Analysis of Nonisothermal Deep Drawing of Aluminum Alloy Sheet With Induced Anisotropy and Rate Sensitivity at Elevated Temperatures
,”
ASME J. Manuf. Sci. Eng.
,
136
(
1
), p.
011006
.
20.
Ganeshmurthy
,
S.
, and
Nassar
,
S. A.
,
2014
, “
Finite Element Simulation of Process Control for Bolt Tightening in Joints With Nonparallel Contact
,”
ASME J. Manuf. Sci. Eng.
,
136
(
2
), p.
021018
.
21.
Tapia
,
G.
, and
Elwany
,
A.
,
2014
, “
A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing
,”
ASME J. Manuf. Sci. Eng.
,
136
(
6
), p.
060801
.
You do not currently have access to this content.