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Citation:
　Kaikui Zheng,Chuanxu Yao,Gang Mou,et al.Prediction of Weld Bead Formation of Duplex Stainless Steel Fabricated by Wire Arc Additive Manufacturing Based on the PSO-BP Neural Network[J].Journal of Marine Science and Application,2023,(2):311-323.[doi:10.1007/s11804-023-00332-y]
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Prediction of Weld Bead Formation of Duplex Stainless Steel Fabricated by Wire Arc Additive Manufacturing Based on the PSO-BP Neural Network

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Title:
Prediction of Weld Bead Formation of Duplex Stainless Steel Fabricated by Wire Arc Additive Manufacturing Based on the PSO-BP Neural Network
Author(s):
Kaikui Zheng1; 2; 3;  Chuanxu Yao1;  Gang Mou1;  Hongliang Xiang1; 2; 3
Affilations:
Author(s):
Kaikui Zheng1; 2; 3;  Chuanxu Yao1;  Gang Mou1;  Hongliang Xiang1; 2; 3
1 School of Advanced Manufacturing, Fuzhou University, Jinjiang 362200, China;
2 Fujian Science&Technology Innovation Laboratory for Optoelectronic Information, Fuzhou 350108, China;
3 School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China
Keywords:
duplex stainless steel; wire arc additive manufacturing; bead forming; prediction model; neural network
分类号:
-
DOI:
10.1007/s11804-023-00332-y
Abstract:
Duplex stainless steel was formed through welding wire and arc additive manufacturing (WAAM) using tungsten inert gas. The effects of wire feeding speed (WFS), welding speed (WS), welding current, and their interaction on the weld bead width and height were discussed. Back-propagation (BP) neural network algorithm prediction model was established by taking the bead width and height as the output layer, and the network weight and threshold values were optimized using the particle swarm optimization (PSO) algorithm to obtain the prediction model of bead width and height. The predicted results were verified by experiments. Results show that the weld bead width increases with the increase in WFS and the welding current and decreases with WS. The smaller the WFS, the faster the WS, which is beneficial for the generation of equiaxed crystals. The smaller the welding current, the faster the cooling speed of the metal melt, which is conducive to the formation of dendrites. The interaction among WS, wire feed speed, and welding current has a significant effect on the bead width. The weld bead height is positively correlated with the wire feed speed and negatively correlated with the WS and current. The interaction between the wire feed speed and WS is significant. The optimized WAAM process parameters for duplex stainless steel are a wire feed speed of 200 cm/min, WS of 24 cm/min, and welding current of 160 A. The maximum error of the BP neural network in predicting the weld bead width and height is 7.74%, and the maximum error between the predicted and experimental values of the BP-PSO neural network is 4.27%. This finding indicates that the convergence speed is fast, improving the prediction accuracy.

References:

Ayarkwa KF, Williams SW, Ding J (2017) Assessing the effect of TIG alternating current time cycle on aluminium wire + arc additive manufacture.Additive Manufacturing, 18:186-193.https://doi.org/10.1016/j.addma.2017.10.005
Cao Y, Zhu S, Liang XB, Wang WL (2010) Overlapping model of beads and curve fitting of bead section for rapid manufacturing by robotic MAG welding process.Robotics and Computer Integrated Manufacturing, 27(3):641-645.https://doi.org/10.1016/j.rcim.2010.11.002
Chen M, He JS, Li JS, Liu HB, Xing SL, Wang G (2022) Excellent cryogenic strength-ductility synergy in duplex stainless steel with heterogeneous lamella structure.Materials Science and Engineering, 831:142335.https://doi.org/10.1016/j.msea.2021.142335
Ding DH, Pan ZX, Cuiuri D, Li HJ (2015) A multi-bead over lapping model for robotic wire and arc additive manufacturing (WAAM).Robotics and Computer Integrated Manufacturing, 31:101-110.https://doi.org/10.1016/j.rcim.2014.08.008
Durga KV, Ram PAVS, Munaf S A, Tanya B (2021) SEM with EDAX analysis on plasma arc welded butt joints of AISI 304 and AISI 316 steels.Materials Today:Proceedings, 44:1350-1355.https://doi.org/10.1016/j.matpr.2020.11.393
Ghafarallahi E, Farrahi GH, Amiri N (2021) Acoustic simulation of ultrasonic testing and neural network used for diameter prediction of three-sheet spot welded joints.Journal of Manufacturing Processes, 64:1507-1516.https://doi.org/10.1016/j.jmapro.2021.03.012
Guo YY, Pan HH, Ren LB, Quan G (2018) An investigation on plasma-MIG hybrid welding of 5083 aluminum alloy.The International Journal of Advanced Manufacturing Technology, 98:1433-1440.https://doi.org/10.1007/s00170-018-2206-4
He HY, Lu J, Zhang Y, Han J, Zhao Z (2021) Quantitative prediction of additive manufacturing deposited layer offset based on passive visual imaging and deep residual network.Journal of Manufacturing Processes, 72:195-202.https://doi.org/10.1016/j.jmapro.2021.09.049
Kumar D, Ghosh S, Kuar AS, Paitandi S (2020) Laser transmission welding of thermoplastic with beam wobbling technique using particle swarm optimization.Materials Today:Proceedings, 26:808-813.https://doi.org/10.1016/j.matpr.2019.12.422
Landowski M, ?wierczyńska A, Rogalski G, Fydrych D (2020) Autogenous Fiber Laser Welding of 316L Austenitic and 2304 Lean Duplex Stainless Steels.Materials 13:2930.https://doi.org/10.3390/ma13132930
Le VT, Mai DS, Doan TK, Paris H (2021) Wire and arc additive manufacturing of 308L stainless steel components:Optimization of processing parameters and material properties.Engineering Science and Technology, an International Journal, 24(4):1015-1026.https://doi.org/10.1016/j.jestch.2021.01.009
Liu LX (2020) Study on Deposition Morphology and digital twin Simulation of Deposition Process in Arc Additive Manufacturing.University of South China, (in Chinese)
Martina F, Ding J, Williams S, Caballero A, Pardal G, Quintino L (2019) Tandem metal inert gas process for high productivity wire arc additive manufacturing in stainless steel.Additive Manufacturing, 25:545-550.https://doi.org/10.1016/j.addma.2018.11.022
Martins M, Casteletti LC (2009) Microstructural characteristics and corrosion behavior of a super duplex stainless steel casting.Materials Characterization, 60(2):150-155.https://doi.org/10.1016/j.matchar.2008.12.010
Maurya AK, Pandey C, Chhibber R (2021) Dissimilar welding of duplex stainless steel with Ni alloys:A review.International Journal of Pressure Vessels and Piping, 192:104439.https://doi.org/10.1016/j.ijpvp.2021.104439
Maurya AK, Pandey C, Chhibber R (2022a) Effect of filler metal composition on microstructural and mechanical characterization of dissimilar welded joint of nitronic steel and super duplex stainless steel.Archives of Civil and Mechanical Engineering, 22:90.https://doi.org/10.1007/s43452-022-00413-9
Maurya AK, Pandey C, Chhibber R (2022b) Influence of heat input on weld integrity of weldments of two dissimilar steels.Materials and Manufacturing Processes, 38:379-400.https://doi.org/10.1080/10426914.2022.2075889
Moreira AF, Ribeiro KSB, Mariani FE, Reginaldo TC (2020) An Initial Investigation of Tungsten Inert Gas (TIG) Torch as Heat Source for Additive Manufacturing (AM) Process.Procedia Manufacturing, 48:671-677.https://doi.org/10.1016/j.promfg.2020.05.159
Qi ZW, Qi BJ, Cong BQ, Sun HY, Zhao G, Ding JL (2019) Microstructure and mechanical properties of wire + arc additively manufactured 2024 aluminum alloy components:Asdeposited and post heat-treated.Journal of Manufacturing Processes, 40:27-36.https://doi.org/10.1016/j.jmapro.2019.03.003
Sahoo A, Tripathy S (2021) Development in plasma arc welding process:A review.Materials Today:Proceedings, 41:363-368.10.1016/j.matpr.2020.09.562
Sasikumar C, Sundaresan R, Merlin MC, Ramakrishnan A (2021) Corrosion study on AA-TIG welding of duplex stainless steel.Materials Today:Proceedings, 45:3383-3385.https://doi.org/10.1016/j.matpr.2020.12.777
?wierczyńska A, Fydrych D, Landowski M, Rogalski G, ?abanowski J (2020) Hydrogen embrittlement of X2CrNiMoCuN25-6-3 super duplex stainless steel welded joints under cathodic protection.Construction and Building Materials, 238:117697.https://doi.org/10.1016/j.conbuildmat.2019.117697
Tian L, Luo Y, Wang Y (2013) Prediction Model of Weld Size of TIG Based on BP Neural Network Optimized by Genetic Algorithm.Journal of Shanghai Jiao Tong University, 47(11):1690-1701 (in Chinese)
Wang C, Zhu P, Lu YH, Shoji T (2022) Effect of heat treatment temperature on microstructure and tensile properties of austenitic stainless 316L using wire and arc additive manufacturing.Materials Science and Engineering, 832:142446.https://doi.org/10.1016/j.msea.2021.142446
Wang YM, Lu J, Zhao Z Deng WX, Han J, Bai LF, Yang XW, Yao JY (2021) Active disturbance rejection control of layer width in wire arc additive manufacturing based on deep learning.Journal of Manufacturing Processes, 67:364-375.https://doi.org/10.1016/j.jmapro.2021.05.005
Xiao Z, Ye SJ, Zhong B, Sun CX (2009) BP neural network with rough set for short term load forecasting.Expert Systems with Applications, 36(1):273-279.https://doi.org/10.1016/j.eswa.2007.09.031
Xue QK, Ma SY, Liang YX, Wang JX, Wang YP, He FL, Liu MY (2018) Weld Bead Geometry Prediction of Additive Manufacturing Based on Neural Network.International Symposium on Computational Intelligence and Design, 2:47-51.https://doi.org/10.1109/ISCID.2018.10112
Yi JQ, Wang Q, Zhang DB, Wen JT (2007) BP neural network prediction-based variable-period sampling approach for networked control systems.Applied Mathematics and Computation, 185(2):976-988.https://doi.org/10.1016/j.amc.2006.07.020
Yuan T, Yu ZL, Chen SJ, Xu M, Jiang XQ (2020) Loss of elemental Mg during wire + arc additive manufacturing of Al-Mg alloy and its effect on mechanical properties.Journal of Manufacturing Processes, 49:456-462.https://doi.org/10.1016/j.jmapro.2019.10.033
Zhao Y, Wang Y, Tang S, Zhang WN, Liu ZY (2019) Edge cracking prevention in 2507 super duplex stainless steel by twin-roll strip casting and its microstructure and properties.Journal of Materials Processing Technology, 266:246-254.https://doi.org/10.1016/j.jmatprotec.2018.11.010

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Last Update: 2023-06-02