Analysis of material property models on WAAM distortion using nonlinear numerical computation and experimental verification with P-GMAW

• Autorzy: Manurung Yupiter H. P. , Prajadhiana Keval P. , Adenan Mohd Shahriman , Awiszus Birgit , Graf Marcel , Haelsig Andre

Identyfikatory

DOI

10.1007/s43452-021-00189-4

• Języki publikacji: EN

Abstrakty

EN

This fundamental research deals with the investigation of material property model influences on distortion induced by multi-layered Wire Arc Additive Manufacturing (WAAM) with synergic-pulsed gas metal arc welding (P-GMAW) process which was modelled and simulated by means of non-linear numerical computation. The material property models of stainless steel SS316L component to be compared stem from three different sources namely existing database, initial wire and evolved component. The new property models were generated with advanced material modelling software JMATPRO based on chemical compositions analysed at initial wire and component using SEM–EDX. The flow curve for each material model was taken with the strain rates ranging from 0.001 to 1.0 s−1. In the numerical simulation, a coupled thermomechanical solution was adopted including phase-change phenomena defined in latent heat. Goldak’s double ellipsoid was applied as heat source model and simplified rectangular bead with hexagonal element type and meshing was developed to avoid extensive pre-processing effort and to reduce the computational time at post-processing level. Temperature behaviour due to the successive layer deposition was simulated considering heat transfer effect coupled to mechanical analysis. The adjustment of simulative transient to experimental thermal distribution lead to new fitted heat transfer coefficient. Prior to execution of numerical simulation, a sensitivity analysis was conducted to find the optimal number of elements or mesh size towards maximum reached temperature. It can be concluded based on the adjusted model, selected mesh size and experimental validation that numerical computation of substrate distortion with evolved material property of component and initial wire of SS316L yield closer average result within the relative error ranging between 11 and 16% compared to database material giving more than 22%.

Słowa kluczowe

EN

WAAM; numerical computation; material property model; distortion; FEM

PL

właściwości materiału; zniekształcenie; obliczenia numeryczne

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2021
• Tom: Vol. 21, no. 1
• Strony: 533--545
• Opis fizyczny: Bibliogr. 36 poz., fot., rys., tab., wykr.

Twórcy

autor

Manurung Yupiter H. P.

• Smart Manufacturing Research Institute (SMRI) & Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia

• https://orcid.org/0000-0002-8090-5736

autor

Prajadhiana Keval P.

• Smart Manufacturing Research Institute (SMRI) & Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia

autor

Adenan Mohd Shahriman

• Smart Manufacturing Research Institute (SMRI) & Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia

autor

Awiszus Birgit

• Professorship of Virtual Production Engineering, Chemnitz University of Technology, Chemnitz, Germany

autor

Graf Marcel

• Chair of Materials and Surface Engineering, Chemnitz University of Technology, Chemnitz, Germany

autor

Haelsig Andre

• Chair of Welding Engineering, Chemnitz University of Technology, Chemnitz, Germany

Bibliografia

• [1] Erhunmwun ID, Ikponmwosa UB. Review on finite element method. J Appl Sci Environ Manag. 2017;21(5):999. https://doi.org/10.4314/jasem.v21i5.30.
• [2] Sun Y, He M, Sun P. Numerical analysis of finite element method for a transient two-phase transport model of polymer electrolyte fuel cell. Proc Comput Sci. 2013;18:2167–76. https://doi.org/10.1016/j.procs.2013.05.387.
• [3] Gu J, et al. Deformation microstructures and strengthening mechanisms for the wire+arc additively manufactured Al-Mg4.5Mn alloy with inter-layer rolling. Mater Sci Eng A. 2018;712(Janu-ary):292–301. https://doi.org/10.1016/j.msea.2017.11.113.
• [4] Rodrigues TA, Duarte V, Miranda RM, Santos TG, Oliveira JP. Current status and perspectives on wire and arc additive manufacturing (WAAM). Materials. 2019;12:7. https://doi.org/10.3390/ma12071121.
• [5] Li JZ, Alkahari MR, Rosli NAB, Hasan R, Sudin MN, Ramli FR. Review of wire arc additive manufacturing for 3D Metal Printing. Int J Autom Technol. 2019;13(3):346–53. https://doi.org/10.1016/j.jmapro.2018.08.001.
• [6] Mehnen J, Ding J, Lockett H, Kazanas P. Design study for wire and arc additive manufacture. Int J Prod Dev. 2014;19(1–3):2–20. https://doi.org/10.1007/s00170-019-03959.
• [7] Ding J, Colegrove P, Mehnen J, Williams S, Wang F, Almeida PS. A computationally efficient finite element model of wire and arc additive manufacture. Int J Adv Manuf Technol. 2014;70(1–4):227–36. https://doi.org/10.1504/IJPD.2014.060028.
• [8] Denlinger ER, Michaleris P. Effect of stress relaxation on distortion in additive manufacturing process modeling. Addit Manuf. 2016;12:51–9. https://doi.org/10.1016/j.addma.2016.06.011.
• [9] Panda B, Shankhwar K, Garg A, Savalani M. Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing. J Intell Manuf. 2016;30:809–20. https://doi.org/10.1007/s10845-016-1282-2.
• [10] Hu Z, Qin X, Li Y, Yuan J, Wu Q. Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing. J Intell Manuf. 2019;40:1–15. https://doi.org/10.1007/s10845-019-01501-z.
• [11] Roman J, Colegrove P, Williams S. Analytical model for distortion prediction in wire + arc additive manufacturing. Residual Stress. 2018;6:277–82. https://doi.org/10.21741/9781945291890-44.
• [12] Graf M, Hälsig A, et al. Thermo-mechanical modelling of wire-arc additive manufacturing (WAAM) of semi-finished products. Metals Open Acess Metallurgy J. 2018;8:12. https://doi.org/10.3390/met8121009.
• [13] Adebayo A, Stephen JT. Effect of distortion on wire and arc additive manfuacturing straight walls. J Build Construct Plann Mater. 2017;2(1):1–7. https://doi.org/10.18488/journal.84.2017.21.1.7.
• [14] Hui H, Ninshu M, et al. Towards arge-scale simulation of residual stress and distortion in wire and arc additive manufacturing. Addit Manufact. 2020;34:5. https://doi.org/10.1016/j.addma.2020.101248.
• [15] Montevecchi F, et al. Finite element mesh coarsening for effective distortion prediction in wire arc additive manufacturing. Addit Manufact. 2017;18:24–33. https://doi.org/10.1016/j.addma.2017.10.010.
• [16] Park SC, Banf HS, Seong WJ. Effect of material properties on angular distortion in wire arc additive manufacturing: experimental and computational analysis. Materials. 2020;13:6. https://doi.org/10.3390/ma13061399.
• [17] Turner R, Schroeder F, Ward RM, Brooks JW. The importance of materials data and modelling parameters in an FE simulation of linear friction welding. Adv Mater Sci Eng. 2014;2014:1–9. https://doi.org/10.1155/2014/521937.
• [18] Lindgren L-E. Finite element modeling and simulation of welding: improved material. J Therm Stress. 2001;24(3):195–231. https://doi.org/10.1080/014957301300006380.
• [19] Pryl D, Cervenka J. Material properties investigation using numerical weld invesitgation. AIP Proc Confer. 2015;3(1):102–8. https://doi.org/10.1016/j.proeng.2015.06.0365.
• [20] Yehorov Y, Da Silva LJ, Scottu A. Exploring the use of switch-back for mitigating homoepitaxial unidirectional grain growth and porosity in WAAM of aluminium alloys. Int J Adv Manuf Technol. 2019;104(1–4):1581–92. https://doi.org/10.1007/s00170-019-03959.
• [21] He T, Yu S, Shi Y, Dai Y. High-accuracy and high-performance WAAM propeller manufacture by cylindrical surface slicing method. Int J Adv Manuf Technol. 2019;105:203–12. https://doi.org/10.1007/s00170-019-04558.
• [22] Wang L, Wei H, Xue JX, Debroy T. Special features of double pulsed gas metal arc welding. J Mater Process Tech-nol. 2017;251:369–75. https://doi.org/10.1016/j.jmatprotec.2017.08.039.
• [23] Banshi PA, Rajeev K. Challenges in application of pulse current gas metal arc welding process for preparation of weld joint with superior quality. Int J Eng Res Technol. 2016;5:1. https://doi.org/10.17577/IJERTV5IS010350.
• [24] Guo J, Xu X, Jepson MAE, Thomson RC. Influence of weld thermal cycle and post weld heat treatment on the microstructure of MarBN steel. Int J Press Vessels Pip. 2019;174:13–24. https://doi.org/10.1016/j.ijpvp.2019.05.010.
• [25] Rankumar KD, Gokul Kumar K. Characterization of metallurgical and mechanical properties on the multi-pass welding of Inconel 625 and AISI 316L. J Mech Sci Technol. 2014;29(3):1039–47. https://doi.org/10.1007/s12206-014-1112-4.
• [26] Hamahmy M, Deiab I. Review and analysis of heat source models for additive manufacturing. Int J Adv Manfuact Technol. 2019;104:1–16. https://doi.org/10.1007/s00170-019-04371-0.
• [27] Bae DH, Kim CH, Cho SY, Hong J, Tsai CL. Numerical analysis of welding residual stress using heat source models for the multi-pass weldment. KSME Int J. 2002;16(9):1054–64. https://doi.org/10.1007/BF02984424.
• [28] Lundback A. Finite element modelling and simulation of welding of aerospace components [Master Thesis]. Sweden: Lulea Univer-sity of Technology; 2003.
• [29] Abid M, Qarni MJ. 3D thermal finite element analysis of single pass girth welded low carbon steel pipe-flange joints. Turk J Eng Environ Sci. 2009;33:281–93. https://doi.org/10.3906/muh-0912-6.
• [30] Graf M, et al. Numerical simulation of metallic wire arc additive manufacturing (WAAM). AIP Conf Proc. 2018;1960:1. https://doi.org/10.1063/1.5035002.
• [31] D V, Durairaj R, . 3D finite element simulation of temperature distribution, residual stress and distortion on 304 stainless steel plates using GTA welding. J Mech Sci Techol. 2016;30:67–76. https://doi.org/10.1007/s12206-015-1208-5.
• [32] Kumar TS, Atikukke N, Kannan R. Thermal cycling effects on the creep-fatigue interaction in type 316LN austenitic stainless steel weld joint. Int J Press Vessels Pip. 2019;178:1040. https://doi.org/10.1016/j.ijpvp.2019.104009.
• [33] Hu Z, Qin X, Shao T. Welding thermal simulation and metallurgical characteristics analysis in WAAM for 5crnimo hot forging die remanufacturing. Proc Eng. 2017;207:2203–8. https://doi.org/10.1016/j.proeng.2017.10.98.
• [34] Jinlong J, Yue Z, Mingye D, Aiping W, Quan L. Numerical simulation on residual stress and deformation for WAAM parts of alluminium alloy based on temperature funtion method. China Welding. 2020;29(2):1–8. https://doi.org/10.12073/j.cw.20191101002.
• [35] Montevecchi F, Scippa A, Campatelli G. Finite element modelling of Wire-arc-additive-manufacturing process. Proc CIRP. 2016. https://doi.org/10.1016/j.procir.2016.08.024.
• [36] Hui H, Jian C, et al (2018) Stress and distortion simulation of additive manufacturing process by high performance computing. In: Proceeding of the ASME 2018 Pressure Vessels and Piping Conference (PVP 2018). https://doi.org/10.1115/PVP2018-85045.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)