Abstract

In practice of caliber rolling, it is highly desirable to obtain optimal processing parameters to achieve the best quality in a short time. A sophisticated and faster simulation model of caliber rolling is proposed, and then a global optimal searching method—simulated annealing algorithm (SA)—is applied to find the optimal processing parameters of caliber rolling. The simulation model of caliber rolling in this paper was established by the back-propagation neural network (BPNN) model, which replaces the complex numerical analytical model, proposed in an earlier paper. This replacement improved the speed of simulation significantly.

Issue Section:
Technical Papers
Keywords:
rolling, simulated annealing, backpropagation, neural nets, production engineering computing, numerical analysis, caliber rolling, complex numerical analytical model, back-propagation neural network, simulated annealing algorithm
Topics:
Algorithms, Artificial neural networks, Modeling, Optimization, Simulated annealing, Stress, Deformation, Simulation
1.
Shivpuri
,
R.
, and
Chou
,
P. C.
, 1989, “
A Comparative Study of Slab, Upper Bound and Finite Element Methods for Predicting Force and Torque in Cold Rolling
”
Int. J. Mach. Tools Manuf.
0890-6955,
29
(
3
), pp.
305
–
322
.
Crossref
Search ADS
 
2.
Kennedy
,
K. F.
, 1988, “
An Approximate Three-Dimensional Metal Flow Analysis for Shape Rolling
,”
J. Eng. Ind.
0022-0817,
110
, pp.
223
–
231
.
Crossref
Search ADS
 
3.
Park
,
J. J.
, and
Oh
,
S. I.
, 1990, “
Application of Three Dimensional Finite Element Analysis to Shape Rolling Processes
,”
J. Eng. Ind.
0022-0817,
112
, pp.
36
–
46
.
Crossref
Search ADS
 
4.
Yoshimura
,
K.
, 1997, “
A Technique for Robust Design of Roll Passes for Consistent Rod Quality
,”
Trans. NAMRI/SME
1047-3025,
XXV
, pp.
55
–
60
.
5.
Kini
,
S. D.
, and
Shivpuri
,
R.
, 1998, “
Roll Pass Design Optimization Applying Fuzzy Reasoning Techniques
,”
Trans. NAMRI/SME
1047-3025,
XXVI
, pp.
79
–
84
.
6.
Kim
,
D. H.
,
Kim
,
D. J.
, and
Kim
,
B. M.
, 1999, “
The Application of Neural Networks and Statistical Methods to Process Design in Metal Forming Processes
,”
Int. J. Adv. Manuf. Technol.
0268-3768,
15
, pp.
886
–
894
.
Crossref
Search ADS
 
7.
Co
,
D. C.
,
Kim
,
D. H.
, and
Kim
,
B. M.
, 1999, “
Application of Artificial Neural Network and Taguchi Method to Preform Design in Metal Forming Considering Workability
,”
Int. J. Mach. Tools Manuf.
0890-6955,
39
, pp.
771
–
785
.
8.
Hsiang
,
S. H.
, and
Lin
,
S. L.
, 2001, “
Application of 3D FEM-slab method to shape rolling
,”
Int. J. Mech. Sci.
0020-7403,
43
, pp.
1155
–
1177
.
Crossref
Search ADS
 
9.
Li
,
G. J.
, and
Kobayashi
,
S.
, 1982, “
Rigid-Plastic Finite-Element Analysis of Plane Strain Rolling
,”
J. Eng. Ind.
0022-0817,
104
, pp.
55
–
64
.
Crossref
Search ADS
 
10.
Tozawa
,
Y.
,
Nakamura
,
M.
, and
Ishikawa
,
T.
, 1976, “
Method of Three-Dimension Analysis and an Applied Example—Analytical Study on Three-Dimension Deformation in Strip Rolling I—
,”
J. Jpn. Soc. Technol. Plast.
0038-1586,
17
(
180
), pp.
37
–
44
.
11.
Chen
,
C. C.
, and
Kobayashi
,
S.
, 1978, “
Rigid Plastic Finite Element Analysis of Ring Compression
,”
Application of Numerical Methods in Forming Processes
,
ASME
,
New York
, AMD-Vol.
28
, pp.
163
–
174
.
12.
Kennedy
,
K. F.
, 1987, “
A Method for Analyzing Spread, Elongation and Bulge in Flat Rolling
,”
J. Eng. Ind.
0022-0817,
109
, pp.
248
–
256
.
Crossref
Search ADS
 
13.
Altan
,
T.
, and
Boulger
,
F. W.
, 1973, “
Flow Stress of Metals and Its Application in Metal Forming Analyses
,”
J. Eng. Ind.
0022-0817,
95
, pp.
1009
–
1019
.
Crossref
Search ADS
 
14.
Altan
,
T.
,
Oh
,
S. I.
, and
Gegel
,
H.
, 1983,
Metal Forming Fundamentals and Applications
, American Society of Metals.
15.
Lee
,
C. H.
, and
Altan
,
T.
, 1972, “
Influence of Flow Stress and Friction Upon Metal Flow in Upset Forging of Ring and Cylinders
,”
J. Eng. Ind.
0022-0817,
94
, pp.
775
–
782
.
Crossref
Search ADS
 
16.
Werbos
,
P.
, 1974, “
Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences
,” PhD thesis, Harvard, Cambridge, MA.
17.
Parker
,
D. B.
, 1985, “
Learning Logic
,” Technical Report TR-47,
Center for Computational Research in Economics and Management Science, MIT
, Cambridge, MA.
18.
Rumelhart
,
D. E.
,
Hinton
,
G. E.
, and
Williams
,
R. J.
, 1986, “
Learning Internal Representation by Error Propagation
,” in
Parallel Distributed Processing
,
D. E.
Rumelhart
 and
McClelland
, eds.,
MIT, Press
,
Cambridge, MA
, Vol.
1
, pp.
318
–
362
.
19.
Kim
,
D. H.
,
Kim
,
D. J.
, and
Kim
,
B. M.
, 1999, “
The Application of Neural Networks and Statistical Methods to Process Design in Metal Forming Processes
,”
Int. J. Adv. Manuf. Technol.
0268-3768,
15
, pp.
886
–
894
.
Crossref
Search ADS
 
20.
Jepsen
,
D. W.
, and
Gelatt
,
C. D.
, Jr.,, 1983, “
Macro Placement by Monte Carlo Annealing
,”
Proceeding of IEEE International Conference on Computer Design
,
Port Chester
, pp.
495
–
498
.
21.
Kirkpatrick
,
S.
,
Gelatt
,
C. D.
, Jr., and
Vecchi
,
M. P.
, 1983, “
Optimization by Simulated Annealing
,”
Science
0036-8075,
220
, pp.
671
–
680
.
Crossref
Search ADS
PubMed
 
22.
ven Laarhoven
,
P. J. M.
, and
Aarts
,
E. H. L.
, 1987,
Simulated Annealing: Theory and Applications
,
Reidel
,
Dordrecht, Holland
.
23.
Azencott
,
R.
, 1992,
Simulated Annealing: Parallelization Techniques
,
Wiley
,
New York
.
24.
Su
,
H.-Z.
, 1987, “
Parameters Seeking in Simulated Annealing
,” M.S. dissertation, Department of Mechanical Engineering, National Taiwan University of Science and Technology.
Copyright © 2007
 by American Society of Mechanical Engineers
You do not currently have access to this content.