Abstract
Bending is the fastest and most efficient process commonly used in the industry for processing thin metal sheets into three-dimensional shapes by localized deformation using only a single geometrical die. However, suppliers provide metal sheets with variations in dimension and mechanical properties, which causes inconsistencies in the final angle after bending. This requires manual checking and correction of each angle, resulting in inefficiency. The problem can be resolved by considering the variations in the sheets and adjusting the bending stroke accordingly. This study used neural network technology to create a model that predicts the final stroke required based on load measurements during the bending process. The model was implemented and validated using a laboratory press. With a root-mean-square error of less than 0.27 deg, the model demonstrates its feasibility for practical industrial applications within the range of its training data.
Issue Section:
Research Papers
References
1.
DIN 6935:2011-10
, 2011
, Cold Bending of Flat Steel Products
, Beuth
, Berlin
.2.
Tan
, Z.
, Persson
, B.
, and Magnusson
, C.
, 1992
, “An Empiric Model for Controlling Springback in V-die Bending of Sheet Metals
,” J. Mater. Process. Technol.
, 34
(1–4
), pp. 449
–455
. 3.
Leu
, D. K.
, and Hsieh
, C. M.
, 2008
, “The Influence of Coining Force on Spring-Back Reduction in V-die Bending Process
,” J. Mater. Process. Technol.
, 196
(1–3
), pp. 230
–235
. 4.
Bruni
, C.
, Forcellese
, A.
, Gabrielli
, F.
, and Simoncini
, M.
, 2006
, “Air Bending of AZ31 Magnesium Alloy in Warm and Hot Forming Conditions
,” J. Mater. Process. Technol.
, 177
(1–3
), pp. 373
–376
. 5.
Salandro
, W. A.
, Bunget
, C.
, and Mears
, L.
, 2011
, “Electroplastic Modeling of Bending Stainless Steel Sheet Metal Using Energy Methods
,” ASME J. Manuf. Sci. Eng.
, 133
(4
), p. 041008
. 6.
Lange
, K.
, 1985
, Handbook of Metal Forming. P. 19.9
, Society of Manufacturing Engineers
, Dearborn, MI
.7.
Schuler
, 1998
, Metal Forming Handbook
, Springer
, Berlin
, pp. 367
–369
.8.
Marciniak
, Z.
, Duncan
, J. L.
, and Hu
, S. J.
, 2002
, Mechanics of Sheet Metal Forming
, 2nd ed., Butterworth-Heinemann
, Oxford
, pp. 93
–94
.9.
Hosford
, W.
, and Caddell
, R.
, 2011
, Metal Forming—Mechanics and Metallurgy
, 4th ed., Cambridge University Press
, Cambridge
, pp. 199
–202
.10.
Vorkov
, V.
, Aerens
, R.
, Vandepitte
, D.
, and Duflou
, J. R.
, 2018
, “Analytical Prediction of Large Radius Bending by Circular Approximation
,” ASME J. Manuf. Sci. Eng.
, 140
(12
), p. 121010
. 11.
Leu
, D.-K.
, 2019
, “Relationship Between Mechanical Properties and Geometric Parameters to Limitation Condition of Springback Based on Springback-Radius Concept in V-die Bending Process
,” Int. J. Adv. Manuf. Technol.
, 101
(1–4
), pp. 913
–926
. 12.
Makinouchi
, A.
, Ogawa
, H.
, and Tozawa
, Y.
, 1989
, “Simulation of Sheet Bending Processes by Elastic-Plastic Finite Element Method
,” CIRP Ann.
, 38
(1
), pp. 279
–282
. 13.
Hsu
, T. C.
, and Shien
, I. R.
, 1997
, “Finite Element Modeling of Sheet Forming Process With Bending Effects
,” J. Mater. Process. Technol.
, 63
(1–3
), pp. 733
–737
. 14.
Uemori
, T.
, Okada
, T.
, and Yoshida
, F.
, 1998
, “Simulation of Springback in V-Bending Process by Elasto-Plastic Finite Element Method With Consideration of Bauschinger Effect
,” Met. Mater. Int.
, 4
(3
), pp. 311
–314
. 15.
Huang
, Y. M.
, and Leu
, D. K.
, 1998
, “Effects of Process Variables on V-die Bending Process of Steel Sheet
,” Int. J. Mech. Sci.
, 40
(7
), pp. 631
–650
. 16.
Garcia-Romeu
, M. L.
, Ciurana
, J.
, and Ferrer
, I.
, 2007
, “Springback Determination of Sheet Metals in an Air Bending Process Based on an Experimental Work
,” J. Mater. Process. Technol.
, 191
(1–3
), pp. 174
–177
. 17.
Srinivasan
, R.
, Vasudevan
, D.
, and Padmanabhan
, P.
, 2013
, “Prediction of Bend Force and Bend Angle in Air Bending of Electrogalvanized Steel Using Response Surface Methodology
,” J. Mech. Sci. Technol.
, 27
(7
), pp. 2093
–2105
. 18.
Özdemir
, M.
, 2020
, “Optimization of Spring Back in Air V Bending Processing Using Taguchi and RSM Method
,” MECHANIKA
, 26
(1
), pp. 73
–81
. 19.
Şenol
, Ö
, Esat
, V.
, and Darendeliler
, H.
, 2014
, “Springback Analysis in Air Bending Process Through Experiment Based Artificial Neural Networks
,” Procedia Eng.
, 81
, pp. 999
–1004
. 20.
Elkins
, K. L.
, and Sturges
, R. H.
, 1999
, “Non-Iterative Control of Small-Radius Bend Angle
,” J. Manuf. Process.
, 1
(1
), pp. 18
–30
. 21.
Elkins
, K. L.
, and Sturges
, R. H.
, 1999
, “Springback Analysis and Control in Small Radius air Bending
,” ASME J. Manuf. Sci. Eng.
, 121
(4
), pp. 679
–688
. 22.
Narayanasamy
, R.
, and Padmanabhan
, P.
, 2012
, “Comparison of Regression and Artificial Neural Network Model for the Prediction of Springback During Air Bending Process of Interstitial Free Steel Sheet
,” J. Intell. Manuf.
, 23
(3
), pp. 357
–364
. 23.
Asmael
, M.
, Fubara
, O. T.
, and Nasir
, T.
, 2023
, “Prediction of Springback Behavior of vee Bending Process of AA5052 Aluminum Alloy Sheets Using Machine Learning
,” Jordan J. Mech. Ind. Eng.
, 17
(1
), pp. 1
–14
. 24.
Teimouri
, R.
, Baseri
, H.
, Rahmani
, B.
, and Bakhshi-Jooybari
, M.
, 2014
, “Modeling and Optimization of Spring-Back in Bending Process Using Multiple Regression Analysis and Neural Computation
,” Int. J. Mater. Form
, 7
(2
), pp. 167
–178
. 25.
Hardt
, D. E.
, 1993
, “Modeling and Control of Manufacturing Processes: Getting More Involved
,” ASME J. Dyn. Syst. Meas. Control
, 115
(2B
), pp. 291
–300
. 26.
Hardt
, D. E.
, 1996
, Manufacturing Processes and Process Control
, MIT Laboratory for Manufacturing and Productivity
, Cambridge, MA.
, pp. 1
–5
.27.
Leu
, D.-K.
, 2015
, “Position Deviation and Springback in V-die Bending Process With Asymmetric Dies
,” Int. J. Adv. Manuf. Technol.
, 79
(5–8
), pp. 1095
–1108
. 28.
Wang
, J.
, Verma
, S.
, Alexander
, R.
, and Gau
, J. T.
, 2008
, “Springback Control of Sheet Metal Air Bending Process
,” J. Manuf. Process.
, 10
(1
), pp. 21
–27
. 29.
ISO 3574:2012
, 2012
, Cold-Reduced Carbon Steel Sheet of Commercial and Drawing Qualities
, ISO
, Geneva
.30.
ASTM A1008/A1008M-21a
, 2021
, Standard Specification for Steel, Sheet, Cold-Rolled, Carbon, Structural, High-Strength Low-Alloy, High-Strength Low-Alloy With Improved Formability, Required Hardness, Solution Hardened, and Bake Hardenable
, ASTM International
, West Conshohocken, PA
.31.
Harlow
, D. G.
, 2004
, “The Effect of Statistical Variability in Material Properties on Springback
,” Int. J. Mater. Prod. Technol.
, 20
(1–3
), pp. 180
–192
. 32.
McCarthy
, J.
, Minsky
, M. L.
, Rochester
, N.
, and Shannon
, C. E.
, 2006
, “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955
,” AI Mag.
, 27
(4
), pp. 12
–14
. 33.
Garcia-Romeu
, M. L.
, and Ciurana
, J.
, 2006
, “Springback and Geometry Prediction—Neural Networks Applied to the Air Bending Process,” Intelligent Computing. ICIC 2006
, Vol. 4113
, D. S.
Huang
, K.
Li
, G. W.
Irwin
, eds., Springer
, Berlin, Heidelberg
, pp. 470
–475
.34.
Inamdar
, M. V.
, Date
, P. P.
, and Desai
, U. B.
, 2000
, “Studies on the Prediction of Springback in Air vee Bending of Metallic Sheets Using an Artificial Neural Network
,” J. Mater. Process. Technol.
, 108
(1
), pp. 45
–54
. 35.
Maind
, S. B.
, and Wankar
, P.
, 2014
, “Research Paper on Basic of Artificial Neural Network
,” Int. J. Recent Innov. Trends Comput.
, 2
(1
), pp. 96
–100
. 36.
Hochreiter
, S.
, and Schmidhuber
, J.
, 1997
, “Long Short-Term Memory
,” Neural Comput.
, 9
(8
), pp. 1735
–1780
. 37.
Olah
, C.
, 2015
, “Understanding LSTM Networks,” https://colah.github.io/posts/2015-08-Understanding-LSTMs/, Accessed August 27, 2015.38.
LeCun
, Y.
, Bottou
, L.
, Bengio
, Y.
, and Haffner
, P.
, 1998
, “Gradient-Based Learning Applied to Document Recognition
,” Proc. IEEE
, 86
(11
), pp. 2278
–2324
. 39.
Shapiro
, S. S.
, and Wilk
, M. B.
, 1965
, “An Analysis of Variance Test for Normality (Complete Samples)
,” Biometrika
, 52
(3–4
), pp. 591
–611
. 40.
Kingma
, D. P.
, and Ba
, J. L.
, 2017
, “Adam: A Method for Stochastic Optimization
,” arXiv:1412.6980v9 [Preprint], 15 p., https://arxiv.org/abs/1412.6980.41.
Liu
, B.
, Shen
, W.
, Li
, P.
, and Zhu
, X.
, 2019
, “Accelerate Mini-Batch Machine Learning Training With Dynamic Batch Size Fitting
,” IJCNN 2019: Proceedings of the International Joint Conference on Neural Networks
, IEEE
, Budapest, Hungary
, July 14–19
, pp. 1
–8
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