An optimization method for defects reduction in fiber laser keyhole welding

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• 作者：Yuewei Ai ; Ping Jiang ; Xinyu Shao ; Chunming Wang ; Peigen Li…
• 刊名：Applied Physics A: Materials Science & Processing
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：122
• 期：1
• 全文大小：1,495 KB
• 参考文献：1.I. Eriksson, J. Powell, A.F.H. Kaplan, Surface tension generated defects in full penetration laser keyhole welding. J. Laser Appl. 26(1), 012006 (2014)CrossRef ADS
  2.J. Rońda, A. Siwek, Modelling of laser welding process in the phase of keyhole formation. Arch. Civ. Mech. Eng. 11(3), 739–752 (2011)CrossRef
  3.X. Li, F. Lu, H. Cui et al., Numerical modeling on the formation process of keyhole-induced porosity for laser welding steel with T-joint. Int. J. Adv. Manuf. Technol. 72(1), 241–254 (2014)CrossRef
  4.P. Norman, H. Engström, A.F.H. Kaplan, Theoretical analysis of photodiode monitoring of laser welding defects by imaging combined with modeling. J. Phys. D Appl. Phys. 41(19), 195502 (2008)CrossRef ADS
  5.K. Kamimuki, T. Inoue, K. Yasuda et al., Prevention of welding defect by side gas flow and its monitoring method in continuous wave Nd: YAG laser welding. J. Laser Appl. 14(3), 136–145 (2002)CrossRef
  6.S. Katayama, M. Mizutani, A. Matsunawa, Development of porosity prevention procedures during laser welding [C] LAMP. Int. Congr. Laser Adv. Mater. Process. 2003, 281–288 (2002)
  7.S. Katayama, Y. Kawahito, M. Mizutani, Elucidation of laser welding phenomena and factors affecting weld penetration and welding defects. Phys. Proced. 5, 9–17 (2010)CrossRef ADS
  8.M. Luo, Y.C. Shin, Estimation of keyhole geometry and prediction of welding defects during laser welding based on a vision system and a radial basis function neural network. Int. J. Adv. Manuf. Technol. 81(1), 1–14 (2015)
  9.K.Y. Benyounis, A.G. Olabi, M.S.J. Hashmi, Optimizing the laser-welded butt joints of medium carbon steel using RSM. J. Mater. Process. Technol. 164, 986–989 (2005)CrossRef
  10.Y. Dongxia, L. Xiaoyan, H. Dingyong et al., Optimization of weld bead geometry in laser welding with filler wire process using Taguchi’s approach. Opt. Laser Technol. 44(7), 2020–2025 (2012)CrossRef ADS
  11.P. Sathiya, K. Panneerselvam, M.Y.A. Jaleel, Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm. Mater. Des. 36, 490–498 (2012)CrossRef
  12.P. Leo, G. Renna, G. Casalino et al., Effect of power distribution on the weld quality during hybrid laser welding of an Al–Mg alloy. Opt. Laser Technol. 73, 118–126 (2015)CrossRef ADS
  13.Y.W. Park, S. Rhee, Process modeling and parameter optimization using neural network and genetic algorithms for aluminum laser welding automation. Int. J. Adv. Manuf. Technol. 37(9), 1014–1021 (2008)CrossRef
  14.D. Westerbaan, D. Parkes, S.S. Nayak et al., Effects of concavity on tensile and fatigue properties in fibre laser welding of automotive steels. Sci. Technol. Weld. Join. 19(1), 60–68 (2014)CrossRef
  15.F. Caiazzo, V. Alfieri, V. Sergi et al., Dissimilar autogenous disk-laser welding of Haynes 188 and Inconel 718 superalloys for aerospace applications. Int. J. Adv. Manuf. Technol. 68(5), 1809–1820 (2013)CrossRef
  16.A. Bahrami, D.T. Valentine, B.T. Helenbrook et al., Study of mass transport in autogenous GTA welding of dissimilar metals. Int. J. Heat Mass Transf. 85, 41–53 (2015)CrossRef
  17.T. Mohandas, R.G. Madhusudhana, Effect of frequency of pulsing in gas tungsten arc welding on the microstructure and mechanical properties of titanium alloy welds. J. Mater. Sci. Lett. 15, 626–628 (1996)CrossRef
  18.V. Balasubramanian, V. Ravisankar, R.G. Madhusudhan, Effect of pulsed current and post weld aging treatment on tensile properties of argon arc welded high strength aluminium alloy. Mater. Sci. Eng. A 459, 19–34 (2007)CrossRef
  19.K. Abdullah, P.M. Wild, J.J. Jeswiet et al., Tensile testing for weld deformation properties in similar gage tailor welded blanks using the rule of mixtures. J. Mater. Process. Technol. 112(1), 91–97 (2001)
  20.C.A. Walsh, Laser welding–literature review. Mater. Sci. Metall. Dep. Univ. Camb. Engl. 2002, 21 (2014)
  21.Y. Kawahito, M. Mizutani, S. Katayama, Elucidation of high-power fibre laser welding phenomena of stainless steel and effect of factors on weld geometry. J. Phys. D Appl. Phys. 40(19), 5854–5859 (2007)CrossRef ADS
  22.Y. Ai, X. Shao, P. Jiang et al., Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials. Appl. Phys. A 121(2), 555–569 (2015)CrossRef ADS
  23.H. Dai, C. MacBeth, Effects of learning parameters on learning procedure and performance of a BPNN. Neural Netw. 10(8), 1505–1521 (1997)CrossRef
  24.Y.S. Park, R. Céréghino, A. Compin et al., Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol. Model. 160(3), 265–280 (2003)CrossRef
  25.C.H. Li, S.C. Park, Combination of modified BPNN algorithms and an efficient feature selection method for text categorization. Inf. Process. Manag. 45(3), 329–340 (2009)CrossRef
  26.W.C. Chen, Y.Y. Hsu, L.F. Hsieh et al., A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN. Expert Syst. Appl. 37(1), 716–726 (2010)CrossRef
  27.V. Vijayaraghavan, A. Garg, C.H. Wong et al., Estimation of mechanical properties of nanomaterials using artificial intelligence methods. Appl. Phys. A 116(3), 1099–1107 (2014)CrossRef ADS
  28.S. Karsoliya, Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. Int. J. Eng. Trends Technol. 3(6), 713–717 (2012)
  29.J.H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence (MIT press, 1992)
  30.L. Gao, F. Lemarchand, M. Lequime, Reverse engineering from spectrophotometric measurements: performances and efficiency of different optimization algorithms. Appl. Phys. A 108(4), 877–889 (2012)CrossRef ADS
  31.S. Akpınar, G.M. Bayhan, A. Baykasoglu, Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks. Appl. Soft Comput. 13(1), 574–589 (2013)CrossRef
  32.O. Ozgun, M. Kuzuoglu, Approximation of transformation media-based reshaping action by genetic optimization. Appl. Phys. A 117(2), 597–604 (2014)CrossRef
  33.M. Galvan-Sosa, J. Portilla, J. Hernandez-Rueda et al., Optimization of ultra-fast interactions using laser pulse temporal shaping controlled by a deterministic algorithm. Appl. Phys. A 114(2), 477–484 (2014)CrossRef ADS
  34.Y. Rong, Z. Zhang, G. Zhang et al., Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA. Opt. Lasers Eng. 67, 94–104 (2015)CrossRef
  35.D. Katherasan, J.V. Elias, P. Sathiya et al., Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm. J. Intell. Manuf. 25(1), 67–76 (2014)CrossRef
  36.D. Parkes, W. Xu, D. Westerbaan et al., Microstructure and fatigue properties of fiber laser welded dissimilar joints between high strength low alloy and dual-phase steels. Mater. Des. 51, 665–675 (2013)CrossRef
  
• 作者单位：Yuewei Ai (1)
  Ping Jiang (1)
  Xinyu Shao (1)
  Chunming Wang (2)
  Peigen Li (1)
  Gaoyang Mi (2)
  Yang Liu (1)
  Wei Liu (1)
  
  1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China
  2. School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China
  
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Condensed Matter
  Optical and Electronic Materials
  Nanotechnology
  Characterization and Evaluation Materials
  Surfaces and Interfaces and Thin Films
  Operating Procedures and Materials Treatment
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-0630

文摘

Laser welding has been widely used in automotive, power, chemical, nuclear and aerospace industries. The quality of welded joints is closely related to the existing defects which are primarily determined by the welding process parameters. This paper proposes a defects optimization method that takes the formation mechanism of welding defects and weld geometric features into consideration. The analysis of welding defects formation mechanism aims to investigate the relationship between welding defects and process parameters, and weld features are considered to identify the optimal process parameters for the desired welded joints with minimum defects. The improved back-propagation neural network possessing good modeling for nonlinear problems is adopted to establish the mathematical model and the obtained model is solved by genetic algorithm. The proposed method is validated by macroweld profile, microstructure and microhardness in the confirmation tests. The results show that the proposed method is effective at reducing welding defects and obtaining high-quality joints for fiber laser keyhole welding in practical production.