Prediction of Bending Angle for Laser Forming of Tailor Machined Blanks by Neural Network

Document Type: Research Paper


1 Arak University of Technology

2 Department of Mechanical Engineering/ Arak University of Technology


Tailor-made blanks are sheet metal assemblies with different thicknesses and/or materials and/or surface coatings. A monolithic sheet can be machined to make the required thickness variations that is referred as tailor machined blanks. Due to the thickness variation in tailor machined blanks, laser bending of these blanks is more complicated than monolithic plates. In this article, laser forming of tailor machined blanks is investigated and an artificial neural network (ANN) will be configured to predict the bending angle of laser formed tailor machined blanks. The input parameters of neural network are selected as start point of scan path, laser irradiating method, laser beam diameter, laser output power and number of radiation passes. The results show that a 5×8×1 trained neural network can predict the bending angle with acceptable accuracy. Comparison of the randomly selected tests with experimental results shows 1.1% error in the prediction of bending angle by trained artificial neural network.


[1] W. Shichun, Z. Jinsong, An experimental study of laser bending for sheet metals, Journal of Materials Processing Technology 110 (2001) 160–163.
[2] J.D. Majumdara, A.K. Nath, I. Manna, Studies on laser bending of stainless steel, Materials Science and Engineering A 385 (2004) 113–122.
[3] B.N. Fetene, V. Kumar, U.S. Dixit, R. Echempati, Numerical and experimental study on multi-pass laser bending of AH36 steel strips, Optics & Laser Technology 99 (2018) 291–300.
[4] K. Maji, D.K. Pratihar, A.K. Nath, Experimental investigations and statistical analysis of pulsed laser bending of AISI 304 stainless steel sheet, Optics & Laser Technology 49 (2013) 18–27.
[5] B.S. Yilbas, S.S. Akhtar, Laser bending of metal sheet and thermal stress analysis, Optics & Laser Technology 61 (2014) 34–44.
[6] A. Gisario, M. Barletta, S. Venettacci, Improvements in springback control by external force laser-assisted sheet bending of titanium and aluminum alloys, Optics & Laser Technology 86 (2016) 46–53.
[7] X.Y. Wang, W.X. Xu, W.J. Xu, Y.F. Hu, Y.D. Liang, L.J. Wang, Simulation and prediction in laser bending of silicon sheet, Transaction of Nonferrous Metals Society of China 21 (2011) s188−s193.
[8] X.Y. Wang, J. Wang, W.J. Xu, D.M. Guo, Scanning path planning for laser bending ofstraight tube into curve tube, Optics & Laser Technology 56 (2014) 43–51.
[9] S.S. Chakraborty, H. More, A.K. Nath, Laser forming of a bowl-shaped surface with a stationary laser beam, Optics and Lasers in Engineering 77 (2016) 126–136.
[10] M. Kreimeyer, F. Wagner, F. Vollertsen, Laser processing of aluminum–titanium tailored blanks, Optics and Lasers in Engineering 43 (2005) 1021–1035.
[11] M. Merklein, M. Johannes, M. Lechner, A. Kuppert, A review on tailored blanks-production, applications and evaluation, Journal of Materials Processing Technology 214 (2014) 151– 164.
[12] A.A. Zadpoor, J. Sinke, R. Benedictus, Experimental and numerical study of machined aluminum tailor-made blanks, Journal of Materials Processing Technology 200 (2008) 288–299.
[13] M. Safari, M. Farzin, Experimental and numerical investigation of laser bending of tailor machined blanks, Optics and Laser Technology 48 (2013) 513–522.
[14] M. Safari, H. Mostaan, M. Farzin, Laser bending of tailor machined blanks: Effect of start point of scan path and irradiation direction relation to step of the blank, Alexandria Engineering Journal 55 (2016) 1587–1594.
[15] R.D. Averett, M.L. Realff, K.I. Jacob, Comparative post fatigue residual property predictions of reinforced and unreinforced poly (ethylene terephthalate) fibers using artificial neural networks, Composites: Part A 41 (2010) 331–344.
[16] Y. Han, W. Zeng, Y. Zhao, X. Zhang, Y. Sun, X. Ma, Modeling of constitutive relationship of Ti–25V–15Cr–0.2Si alloy during hot deformation process by fuzzy-neural network, Materials and Design 31 (2010) 4380–4385.
[17] O. Sabokpa, A. Zarei-Hanzaki, H.R. Abedi, N. Haghdadi, Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy, Materials and Design 39 (2012) 390–396.
[18] N. Haghdadi, A. Zarei-Hanzaki, A.R. Khalesian, H.R. Abedi, Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy, Materials and Design 49 (2013) 386–391.
[19] F. Abbassi, T. Belhadj, S. Mistou, A. Zghal, Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming, Materials and Design 45 (2013) 605–615.
[20] P.J. Cheng, S.C. Lin, Using neural networks to predict bending angle of sheet metal formed by laser, International Journal of Machine Tools & Manufacture 40 (2000) 1185–1197.
[21] A. Gisario, M. Barletta, C. Conti, S. Guarino, Springback control in sheet metal bending by laser-assisted bending: Experimental analysis, empirical and neural network modeling, Optics and Lasers in Engineering 49 (2011) 1372–1383.
[22] M. Safari, M. Farzin, H. Mostaan, A novel method for laser forming of two-step bending of a dome shaped part, Iranian Journal of Materials Forming 4 (2017) 1–14.
[23] M. Safari, M. Ebrahimi, Numerical investigation of laser bending of perforated sheets, International Journal of Advanced Design and Manufacturing Technology 9 (2016) 53–60.