Analysis and synthesis of laser forming process using neural networks and neurofuzzy inference system
 Kuntal Maji,
 D. K. Pratihar,
 A. K. Nath
 … show all 3 hide
Rent the article at a discount
Rent now* Final gross prices may vary according to local VAT.
Get AccessAbstract
To apply laser forming process in reality, it is required to know the relationships between the deformed shape and scanning paths along with heating conditions. The deformation due to laser scanning depends on various factors, namely laser power, scan speed, spot diameter, scan position, number of scans, and many others. This article presents soft computingbased methods to predict deformations for a set of heating conditions, and also to determine the heating lines and heat conditions, in order to get a desired shape (i.e., inverse analysis). A novel attempt has been made in this paper to carry out analysis and synthesis (inverse analysis) of laser forming process using both geneticneural network (GANN) and genetic adaptive neurofuzzy inference system (GAANFIS). During the analysis, laser power, scan speed, spot diameter, scan position and number of scans are taken as inputs and bending angle is considered as the output. A batch mode of training has been used for both the approaches with the help of some experimental data. The performances of the developed approaches have been tested on some real experimental data. Both the approaches are found to be effective to predict the bending angles and carry out the process synthesis successfully. GANN approach is found to perform better than the GAANFIS approach in predicting the bending angles, and both the approaches are able to provide comparable predictions in inverse analysis.
 Akbilgic, O, Bozdogan, H (2011) Predictive subset selection using regression trees and RBF neural networks hybridized with the genetic algorithm. Eur J Pure Appl Math 4: pp. 467485
 Carlone, P, Palazzo, GS, Pasquino, R (2008) Inverse analysis of the laser forming process by computational modeling and methods. Comput Math Appl 55: pp. 20182032 CrossRef
 Casalino, G, Ludovico, AD (2002) Parameter selection by an artificial neural network for a laser bending process. IMechE Part B J Eng Manuf 216: pp. 15171520 CrossRef
 Chen, DJ, Xiang, YB, Wu, SC, Li, MQ (2002) Application of fuzzy neural network to laser bending process of sheet metal. Mater Sci Technol 18: pp. 677680 CrossRef
 Cheng, PJ, Lin, SC (2000) Using neural networks to predict bending angle of sheet metal formed by laser. Int J Mach Tools Manuf 40: pp. 11851197 CrossRef
 Cheng, PJ, Lin, SC (2001) An analytical model to estimate angle formed by laser. J Mater Process Technol 108: pp. 314319 CrossRef
 Cheng, JG, Yao, YL (2004) Process synthesis of laser forming by genetic algorithm. Int J Mach Tools Manuf 44: pp. 16191628 CrossRef
 Dragos V, Dan V, Kovacevic R (2000) Prediction of the laser sheet bending using neural network. In: IEEE international symposium on circuits and systems, pp 686–689
 Du Y, Wang X (2010) Improved BP network to predict bending angle in the laser bending process for sheet metal. In: International conference on intelligent system design and engineering application, Cairo, Egypt, pp 839–843
 Gisario, A, Barletta, M, Conti, C, Guarino, S (2011) Springback control in sheet metal bending by laserassisted bending: experimental analysis, empirical and neural network modeling. Opt Lasers Eng 49: pp. 13721383 CrossRef
 Griffiths, J, Edwardson, SP, Dearden, G, Watkins, KG (2010) Finite element modeling of laser forming at macro and micro scales. Phys Procedia 5: pp. 371380 CrossRef
 Guarino, S, Ucciardello, N, Tagliaferri, V (2007) An application of neural network solutions to modeling of diode laser assisted forming process of AA6082 thin sheets. Key Eng Mater 344: pp. 325332 CrossRef
 Herrera, F, Lozano, M, Verdegay, JL (1995) Tuning fuzzy logic controllers by genetic algorithms. Int J Approx Reason 12: pp. 293315 CrossRef
 Hu, Z, Labudovic, M, Wang, H, Kovacevic, R (2001) Computer simulation and experimental investigation of sheet metal bending using laser beam scanning. Int J Mach Tools Manuf 41: pp. 589607 CrossRef
 Hu, J, Dang, D, Shen, H, Zhang, Z (2012) A finite element model using multilayered shell element in laser forming. Opt Laser Technol 44: pp. 11481155 CrossRef
 Jang, JSR (1993) ANFIS: adaptivenetworkbased fuzzy inference system. IEEE Trans Syst Man Cybern 23: pp. 665685 CrossRef
 Keller, JM, Yager, RR, Tahani, H (1992) Neural nework implementation of fuzzy logic. Fuzzy Sets Syst 45: pp. 112 CrossRef
 Khashei, M, Bijari, M (2010) An artificial neural network (p, d, q) model for time series forecasting. Expert Syst Appl 37: pp. 479489 CrossRef
 Kuo, HC, Wu, LJ (2002) Automation of heat bending in shipbuilding. Comput Ind 48: pp. 127142 CrossRef
 Kyrsanidi, AK, Kermanidis, TB, Pantelakis, SG (2000) An analytical model for the prediction of distortions caused by the laser forming process. J Mater Process Technol 104: pp. 94102 CrossRef
 Liu, C, Yao, YL (2002) Optimal and robust design of the laser forming process. J Manuf Processes 4: pp. 5266 CrossRef
 Maji, K, Pratihar, DK (2010) Forward and reverse mappings of electrical discharge machining process using adaptive networkbased fuzzy inference system. Expert Syst Appl 37: pp. 85668574 CrossRef
 Maji K, Pratihar DK, Nath AK (2012) Experimental investigations, modeling and optimization of multiscan laser forming of AISI 304 stainless steel sheet. Int J Adv Manuf Technol (under review)
 Martino, FD, Loia, V, Sessa, S (2010) Fuzzy transforms method and attribute dependency in data analysis. Inf Sci 180: pp. 493505 CrossRef
 Martino, FD, Loia, V, Sessa, S (2011) Fuzzy transforms method in prediction data analysis. Fuzzy Sets Syst 180: pp. 146163 CrossRef
 Montgomery, DC (2001) Design and analysis of experiments. Wiley, New York
 Nasrabadi, E, Hashemi, SM (2008) Robust fuzzy regression analysis using neural networks. Int J Uncertain Fuzziness Knowl Based Syst 16: pp. 579598 CrossRef
 Nguyen, TT, Yang, YS, Bae, KY, Choi, SN (2009) Prediction of deformations of steel plate by artificial neural network in forming process with induction heating. J Mech Sci Technol 23: pp. 12111221 CrossRef
 Pratihar, DK (2008) Soft computing. Narosa Publishing House, New Delhi
 Pratihar, DK, Deb, K, Ghosh, A (1999) A geneticfuzzy approach for mobile robot navigation among moving obstacles. Int J Approx Reason 20: pp. 145172 CrossRef
 Shen, H, Vollertsen, F (2009) Modeling of laser forming—a review. Comput Mater Sci 46: pp. 834840 CrossRef
 Shen, H, Shi, Y, Yao, Z, Hu, J (2006) An analytical model for estimating deformation in laser forming. Comput Mater Sci 37: pp. 593598 CrossRef
 Shen, H, Shi, YJ, Yao, ZQ, Hu, J (2006) Fuzzy logic model for bending angle in laser forming. Mater Sci Technol 22: pp. 981986 CrossRef
 Vollertsen, F (1994) An analytical model for laser bending. Lasers Eng 2: pp. 261276
 Vollertsen F, Geiger M, Li WM (1993) FDMand FEM simulation of laser forming: a comparative study. In: Proceedings of the fourth international conference on technology of plasticity, pp 1793–1798
 Wang, X, Xu, W, Chen, H, Wang, J (2008) Parameter prediction in laser bending of aluminum alloy sheet. Front Mech Eng China 3: pp. 293298 CrossRef
 Whitley, D, Starkweather, T, Bogart, C (1990) Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Comput 14: pp. 347361 CrossRef
 Wu, S, Ji, Z (2002) FEM simulation of the deformation field during the laser forming of sheet metal. J Mater Process Technol 121: pp. 269272 CrossRef
 Zhang, L, Michaleris, P (2004) Investigation of Lagrangian and Eulerian finite element methods for modeling the laser forming process. Finite Element Anal Des 40: pp. 383405 CrossRef
 Zhang, P, Guo, B, Shan, DB, Ji, Z (2007) FE simulation of laser curve bending of sheet metals. J Mater Process Technol 184: pp. 157162 CrossRef
 Title
 Analysis and synthesis of laser forming process using neural networks and neurofuzzy inference system
 Journal

Soft Computing
Volume 17, Issue 5 , pp 849865
 Cover Date
 20130501
 DOI
 10.1007/s0050001209497
 Print ISSN
 14327643
 Online ISSN
 14337479
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Laser forming
 Analysis
 Synthesis
 Neural networks
 Neurofuzzy inference system
 Genetic algorithm
 Industry Sectors
 Authors

 Kuntal Maji ^{(1)}
 D. K. Pratihar ^{(1)}
 A. K. Nath ^{(1)}
 Author Affiliations

 1. Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, Kharagpur, 721 302, India