[1] Pan Minghui, Liao Wenhe, Xing Yan, et al. Mapping relationship analysis of welding assembly properties forthin-walled parts with finite element and machine learning algorithm [J]. Journal of Southeast University (English Edition), 2022, 38 (2): 126-136. [doi:10.3969/j.issn.1003-7985.2022.02.004]

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Mapping relationship analysis of welding assembly properties forthin-walled parts with finite element and machine learning algorithm()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

38

Issue:

2022 2

Page:

126-136

Research Field:

Mechanical Engineering

Publishing date:

2022-06-20

Info

Title:

Mapping relationship analysis of welding assembly properties forthin-walled parts with finite element and machine learning algorithm

Author(s):

Pan Minghui¹;  2;  Liao Wenhe ¹;  2;  Xing Yan³;  Tang Wencheng³

¹School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
²Digital Forming Technology and Equipment National-Local United Engineering Laboratory, Nanjing University of Science and Technology, Nanjing 210094, China
³School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Keywords:

parallel T-shaped thin-walled parts;  welding assembly property;  finite element analysis;  mapping relationship;  machine learning algorithm

PACS:

-

DOI:

10.3969/j.issn.1003-7985.2022.02.004

Abstract:

The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure. The effects of welding direction, clamping, fixture release time, fixed constraints, and welding sequences on these properties were analyzed, and the mapping relationship among welding characteristics was thoroughly examined. Different machine learning algorithms, including the generalized regression neural network(GRNN), wavelet neural network(WNN), and fuzzy neural network(FNN), are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship. Compared with those from GRNN and WNN, the maximum mean relative errors for the predicted values of deformation, temperature, and residual stress with FNN were less than 4.8%, 1.4%, and 4.4%, respectively. These results indicate that FNN generated the best predicted welding characteristics. Analysis under various welding conditions also shows a mapping relationship among welding deformation, temperature, and residual stress over a period of time. This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.

References:

[1] Ma N S, Wang J C, Okumoto Y. Out-of-plane welding distortion prediction and mitigation in stiffened welded structures[J]. The International Journal of Advanced Manufacturing Technology, 2016, 84(5/6/7/8): 1371-1389. DOI:10.1007/s00170-015-7810-y.
[2] Li Y W, Zou W F, Lee B, et al. Research progress of aluminum alloy welding technology[J]. The International Journal of Advanced Manufacturing Technology, 2020, 109(5/6): 1207-1218. DOI:10.1007/s00170-020-05606-1.
[3] Murakawa H, Deng D A, Ma N S, et al. Applications of inherent strain and interface element to simulation of welding deformation in thin plate structures[J]. Computational Materials Science, 2012, 51(1): 43-52. DOI:10.1016/j.commatsci.2011.06.040.
[4] Ma N S, Huang H, Murakawa H. Effect of jig constraint position and pitch on welding deformation[J]. Journal of Materials Processing Technology, 2015, 221: 154-162. DOI:10.1016/j.jmatprotec.2015.02.022.
[5] Ma N S, Cai Z P, Huang H, et al. Investigation of welding residual stress in flash-butt joint of U₇₁Mn rail steel by numerical simulation and experiment[J]. Materials & Design, 2015, 88: 1296-1309. DOI:10.1016/j.matdes.2015.08.124.
[6] Bhatti A A, Barsoum Z, Murakawa H, et al. Influence of thermo-mechanical material properties of different steel grades on welding residual stresses and angular distortion[J]. Materials & Design, 2015, 65: 878-889. DOI:10.1016/j.matdes.2014.10.019.
[7] Chen B Q, Hashemzadeh M, Garbatov Y, et al. Numerical and parametric modeling and analysis of weld-induced residual stresses[J]. International Journal of Mechanics and Materials in Design, 2015, 11(4): 439-453. DOI:10.1007/s10999-014-9269-7.
[8] Xia J, Jin H. Numerical study of welding simulation and residual stress on butt welding of dissimilar thickness of austenitic stainless steel[J]. The International Journal of Advanced Manufacturing Technology, 2017, 91(1/2/3/4): 227-235. DOI:10.1007/s00170-016-9738-2.
[9] Cheon J, Na S J. Prediction of welding residual stress with real-time phase transformation by CFD thermal analysis[J]. International Journal of Mechanical Sciences, 2017, 131/132: 37-51. DOI:10.1016/j.ijmecsci.2017.06.046.
[10] Mondal A K, Biswas P, Bag S. Prediction of welding sequence induced thermal history and residual stresses and their effect on welding distortion[J]. Welding in the World, 2017, 61(4): 711-721. DOI:10.1007/s40194-017-0468-3.
[11] Lee J M, Seo H D, Chung H. Efficient welding distortion analysis method for large welded structures[J]. Journal of Materials Processing Technology, 2018, 256: 36-50. DOI:10.1016/j.jmatprotec.2018.01.043.
[12] Ahmad A S, Wu Y X, Gong H, et al. Numerical simulation of thermal and residual stress field induced by three-pass TIG welding of Al 2219 considering the effect of interpass cooling[J]. International Journal of Precision Engineering and Manufacturing, 2020, 21(8): 1501-1518. DOI:10.1007/s12541-020-00357-1.
[13] Arunkumar M, Dhinakaran V, Siva Shanmugam N. Numerical prediction of temperature distribution and residual stresses on plasma arc welded thin titanium sheets[J]. International Journal of Modelling and Simulation, 2021, 41(2): 146-162. DOI:10.1080/02286203.2019.1700089.
[14] Pan M H, Tang W C, Xing Y. Welding thermal characteristics analysis with numerical simulation for thin-wall parts assembly under different conditions[J]. Journal of Southeast University(English Edition), 2018, 34(2):199-207. DOI:10.3969/j.issn.1003-7985.2018.02.009.
[15] Majumder H, Maity K P. Predictive analysis on responses in WEDM of titanium grade 6 using general regression neural network(GRNN)and multiple regression analysis(MRA)[J]. Silicon, 2018, 10(4): 1763-1776. DOI:10.1007/s12633-017-9667-1.
[16] Saravanakumar A, Rajeshkumar L, Balaji D, et al. Prediction of wear characteristics of AA2219-Gr matrix composites using GRNN and Taguchi-based approach[J]. Arabian Journal for Science and Engineering, 2020, 45(11): 9549-9557. DOI:10.1007/s13369-020-04817-8.
[17] Pan M H, Tang W C, Xing Y. The deformation analysis, prediction, and experiment verification for thin-wall part assembly based on the fractal theory model with WNNM[J].The International Journal of Advanced Manufacturing Technology, 2017, 92(9/10/11/12): 4145-4159. DOI:10.1007/s00170-017-0497-5.
[18] Yao R H, Zhang W S, Zhang L H. Hybrid methods for short-term traffic flow prediction based on ARIMA-GARCH model and wavelet neural network[J]. Journal of Transportation Engineering, Part A: Systems, 2020, 146(8): 04020086. DOI:10.1061/jtepbs.0000388.
[19] Han Y F, Zeng W D, Zhao Y Q, et al. A study on the prediction of mechanical properties of titanium alloy based on adaptive fuzzy-neural network[J]. Materials & Design, 2011, 32(6): 3354-3360. DOI:10.1016/j.matdes.2011.02.009.
[20] Li W, Qiao J F, Zeng X J, et al. Identification and simplification of T-S fuzzy neural networks based on incremental structure learning and similarity analysis[J]. Fuzzy Sets and Systems, 2020, 394:65-86. DOI:10.1016/j.fss.2019.10.003.

Memo

Memo:

Biography: Pan Minghui(1986—), male, doctor, lecturer, mhpan@njust.edu.cn.
Foundation items: The Natural Science Foundation of Jiangsu Province, China(No.BK20200470), China Postdoctoral Science Foundation(No.2021M691595), Innovation and Entrepreneurship Plan Talent Program of Jiangsu Province(No.AD99002).
Citation: Pan Minghui, Liao Wenhe, Xing Yan, et al. Mapping relationship analysis of welding assembly properties for thin-walled parts with finite element and machine learning algorithm[J].Journal of Southeast University(English Edition), 2022, 38(2):126-136.DOI:10.3969/j.issn.1003-7985.2022.02.004.

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