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Numerical Prediction of Residual Deformation and Failure for Powder Bed Fusion Additive Manufacturing of Metal Parts

Published online by Cambridge University Press:  06 August 2020

D.D. Lyu ,
W. Hu ,
B. Ren ,
X.F. Pan  and
C. T. Wu Open the ORCID record for C. T. Wu [Opens in a new window]
D.D. Lyu
Affiliation:
Computational and Multiscale Mechanics Group Livermore Software Technology, an ANSYS company, Livermore, U. S. A.
W. Hu
Affiliation:
Computational and Multiscale Mechanics Group Livermore Software Technology, an ANSYS company, Livermore, U. S. A.
B. Ren
Affiliation:
Computational and Multiscale Mechanics Group Livermore Software Technology, an ANSYS company, Livermore, U. S. A.
X.F. Pan
Affiliation:
Computational and Multiscale Mechanics Group Livermore Software Technology, an ANSYS company, Livermore, U. S. A.
C. T. Wu*
Affiliation:
Computational and Multiscale Mechanics Group Livermore Software Technology, an ANSYS company, Livermore, U. S. A.
*
*Corresponding author (CT.Wu@ansys.com)
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Abstract

Residual deformation and failure are two critical issues in powder bed fusion (PBF) additive manufacturing (AM) of metal products. Residual deformation caused by the non-uniform residual stress distribution dramatically affects the quality of AM product and can result in catastrophic failure in operation. Therefore, the development of an effective numerical approach to predict residual deformation and failure characteristics of AM product is always a major concern in industrial applications.

In this paper, a numerical approach in predicting residual distortion, stress and failure in AM products is presented. The conventional inherent strain method used in welding process is modified to consider the specific characteristic of AM process, such as the influences of reheating and scanning pattern. This approach consists of three simulation steps including a detailed process simulation in small-scale, a onetime static mechanical finite element analysis in part-scale, and a material failure analysis. First, the inherent strains are calculated from a thermo-mechanical process simulation in small-scale, which considers AM process parameters, such as laser power, scanning speed and path. The physical state in deposited materials including powder, liquid and solid states are taken into account in the simulation by specifying the solidus and liquidus temperature and corresponding material properties. Then the inherent strains are applied layer by layer to the part-scale simulation, where the residual distortion and stress can be predicted efficiently. Finally, a Lagrange particle method is utilized to study the failure characteristics of AM products. Numerical examples are studied to investigate the effectiveness and applicability of present approach.

Keywords

Inherent strainResidual deformationFailurePowder bed fusion additive manufacturing
Type
Research Article
Information
Journal of Mechanics , Volume 36 , Issue 5 , October 2020 , pp. 623 - 636
Copyright
Copyright © 2020 The Society of Theoretical and Applied Mechanics

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References

REFERENCES

Markl, M. and Körner, C., “Multiscale modeling of powder-bed-based additive manufacturing,” Annual Review of Materials Research, 46, pp. 1-34 (2016).CrossRefGoogle Scholar
Wu, A. S., Brown, D.W., Kumar, M., Gallegos, G.F. and King, W.E., “An experimental investigation into additive manufacturing-induced residual stresses in 316L stainless steel,” Metallurgical and Materials Transactions A, 45, pp. 6260-6270 (2014).CrossRefGoogle Scholar
Sochalski-Kolbus, L.M., Payzant, E.A., Cornwell, P.A., Watkins, T.R., Babu, S.S., Dehoff, R.R., Lorenz, M., Ovchinnikova, O. and Duty, C., “Comparison of residual stresses in Inconel 718 simple parts made by electron beam melting and direct laser metal sintering,” Metallurgical and Materials Transactions A, 46, pp. 1419-1432 (2015).CrossRefGoogle Scholar
Kruth, J. P., Froyen, L., van Vaerenbergh, J., Mercelis, P., Rombouts, M., and Lauwers, B., “Selective laser melting of iron-based powder,” Journal of materials processing technology, 149, pp. 616-622 (2004).CrossRefGoogle Scholar
Fu, C. H. and Guo, Y. B., “3-dimensional finite element modeling of selective laser melting Ti-6Al- 4V alloy,” 25th Annual International Solid Freeform Fabrication Symposium, pp. 1129-1144 (2014).Google Scholar
Megahed, M., Mindt, H.W., N’Dri, N., Duan, H. and Desmaison, O., “Metal additive-manufacturing process and residual stress modeling,” Integrating Materials and Manufacturing Innovation, 5, pp. 61-93 (2016).CrossRefGoogle Scholar
Papadakis, L., Loizou, A., Risse, J., Bremen, S. and Schrage, J., “A computational reduction model for appraising structural effects in selective laser melting manufacturing: a methodical model reduction proposed for time-efficient finite element analysis of larger components in Selective Laser Melting,” Virtual and Physical Prototyping, 9, pp. 17-25 (2014).CrossRefGoogle Scholar
Li, C., Liu, J.F., Fang, X.Y. and Guo, Y.B., “Efficient predictive model of part distortion and residual stress in selective laser melting,” Additive Manufacturing, 17, pp. 157-168 (2017).CrossRefGoogle Scholar
Denlinger, E.R., Irwin, J. and Michaleris, P., “Thermomechanical modeling of additive manufacturing large parts,” Journal of Manufacturing Science and Engineering, 136, pp. 061007 (2014).CrossRefGoogle Scholar
Denlinger, E.R., Gouge, M., Irwin, J. and Michaleris, P., “Thermomechanical model development and in situ experimental validation of the Laser Powder-Bed Fusion process,” Additive Manufacturing, 16, pp. 73-80 (2017).CrossRefGoogle Scholar
Ueda, Y., Fukuda, K., Nakacho, K. and Endo, S., “A new measuring method of residual stresses with the aid of finite element method and reliability of estimated values,” Journal of the Society of Naval Architects of Japan, 138, pp. 499-507 (1975).CrossRefGoogle Scholar
Keller, N. and Ploshikhin, V., “New method for fast predictions of residual stress and distortion of AM parts,” Solid Freeform Fabrication Symposium, pp. 1229-1237 (2014).Google Scholar
Alvarez, P., Ecenarro, J., Setien, I., San Sebastian, M., Echeverria and A., Eciolaza, L., “Computationally efficient distortion prediction in powder bed fusion additive manufacturing,” International Journal of Engineering Research & Science, 2, pp. 39-46 (2016).Google Scholar
Setien, I., Chiumenti, M., van der Veen, S., San Sebastian, M., Garciandia, F. and Echeverria, A., “Empirical methodology to determine inherent strains in additive manufacturing,” Computers and Mathematics with Applications, 78, pp. 2282-2295 (2019).CrossRefGoogle Scholar
Wu, C.T., Koishi, M. and Hu, W., “A displacement smoothing induced strain gradient stabilization for the meshfree Galerkin nodal integration method,” Computational Mechanics, 56, pp. 19-37 (2015).CrossRefGoogle Scholar
Wu, C.T., Wu, Y. and Koishi, M., “A strain-morphed nonlocal meshfree method for the regularized particle simulation of elastic-damage induced strain localization problems,” Computational Mechanics, 56, pp. 1039-1054 (2015).CrossRefGoogle Scholar
Wu, C.T., Chi, S.W., Koishi, M. and Wu, Y., “Strain gradient stabilization with dual stress points for the meshfree nodal integration method in inelastic analyses,” International Journal for Numerical Methods in Engineering, 107, pp. 3-30 (2016).Google Scholar
Kim, K.S., Moo-Hyun, Kim, Jang, H. and Cho, H.C., “Simulation of solid particle interactions including segregated lamination by using MPS method,” CMES: Computer Modeling in Engineering & Sciences, 116, pp. 11-29 (2018).CrossRefGoogle Scholar
Liu, Y., Qiao, Y., and Li, T., ”A correct smoothed particle method to model structure-ice interaction,” CMES: Computer Modeling in Engineering & Sciences, 120, pp. 177-201 (2019).CrossRefGoogle Scholar
Song, Y., Yan, J., Li, S. and Kang, Z., ”Peridynamic modeling and simulation of ice craters by impact,” CMES: Computer Modeling in Engineering & Sciences, 121, pp. 465-492 (2019).CrossRefGoogle Scholar
Wu, C.T., Ma, N., Takada, K. and Okada, H., “A meshfree continuous-discontinuous approach for the ductile fracture modeling in explicit dynamics analysis,” Computational Mechanics, 58, pp. 391-409 (2016).CrossRefGoogle Scholar
Wu, C.T. and Ren, B., “A stabilized non-ordinary state-based peridynamics for the nonlocal ductile material failure analysis in metal machining process,” Computer Methods in Applied Mechanics and Engineering, 291, pp. 197-215 (2015).CrossRefGoogle Scholar
Wu, C.T., Wu, Y., Crawford, J.E. and Magallanes, J.M., “Three-dimensional concrete impact and penetration simulations using the smoothed particle Galerkin method,” International Journal of Impact Engineering, 106, pp. 1-17 (2017).CrossRefGoogle Scholar
Jia, B., Ju, L. and Wang, Q., “Numerical simulation of dynamic interaction between ice and wide vertical structure based on peridynamics,” CMES: Computer Modeling in Engineering & Sciences, 121, pp. 501-522 (2019).CrossRefGoogle Scholar
Cheng, Z., Wang, Z. and Luo, Z., “Dynamic fracture analysis for shale material by peridynamic modelling,” CMES: Computer Modeling in Engineering & Sciences, 118, pp. 509-527 (2019).CrossRefGoogle Scholar
Yuan, M.G. and Ueda, Y., “Prediction of residual stresses in welded T-and I-joints using inherent strains,” Journal of engineering materials and technology, 118, pp. 229-234 (1996).CrossRefGoogle Scholar
Deng, D., Murakawa, H. and Liang, W., “Numerical simulation of welding distortion in large structures,” Computer methods in applied mechanics and engineering, 196, pp. 4613-4627 (2007).CrossRefGoogle Scholar
Murakawa, H., Deng, D., Ma, N. and Wang, J., “Applications of inherent strain and interface element to simulation of welding deformation in thin plate structures,” Computational Materials Science, 51, pp. 43-52 (2012).CrossRefGoogle Scholar
Liang, X., Cheng, L., Chen, Q., Yang, Q. and To, A.C., “A modified method for estimating inherent strains from detailed process simulation for fast residual distortion prediction of single-walled structures fabricated by directed energy deposition,” Additive Manufacturing, 23, pp. 471-486 (2018).CrossRefGoogle Scholar
Keller, N. and Ploshikhin, V., “New method for fast predictions of residual stress and distortion of AM parts,” In Solid Freeform Fabrication Symposium (SFF), Texas, U.S.A. (Aug 4-6, 2014).Google Scholar
Bugatti, M. and Semeraro, Q., “Limitations of the inherent strain method in simulating powder bed fusion processes,” Additive Manufacturing, 23, pp. 329-346 (2018).CrossRefGoogle Scholar
Denlinger, E.R., “Thermo-mechanical model development and experimental validation for metallic parts in additive manufacturing,” Ph.D. Dissertation, The Pennsylvania State University, Pennsylvania, U.S.A. (2015).Google Scholar
Marimuthu, S.et al, “Finite element modeling of substrate thermal distortion in direct laser additive manufacture of an aero-engine component,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 227, pp. 1987-1999 (2013).Google Scholar
Fallah, V., Alimardani, M., Corbin, S.F. and Khajepour, A., “Temporal development of melt-pool morphology and clad geometry in laser powder deposition,” Computational Materials Science, 50, pp. 2124-2134 (2011).CrossRefGoogle Scholar
Goldak, J., Chakravarti, A. and Bibby, M., “A new finite element model for welding heat sources,” Metallurgical transactions B, 15, pp. 299-305 (1984).CrossRefGoogle Scholar
Lindstrom, P.R.M, “DNV Platform of Computational Welding Mechanics,” Proceedings of International Institute of Welding 66th Annual Assembly, (2013).Google Scholar
Lindstrom, P.R.M., “Improved CWM platform for modeling welding procedures and their effects on structural behavior,” Ph.D. Dissertation, Production Technology, University West, Trollhattan, Sweden (2015).Google Scholar
Touloukian, Y.S. and Ho, C.Y., Thermophysical properties of matter - the TPRC data series. Volume 1. Thermal conductivity - metallic elements and alloys, New York, U.S.A. (1970).CrossRefGoogle Scholar
Touloukian, Y.S. and Buyco, E.H., Thermophysical Properties of Matter - The TPRC Data Series. Volume 4. Specific Heat - Metallic Elements and Alloys, New York, U.S.A. (1970).Google Scholar
Wang, L., Jiang, X., Zhu, Y., Zhu, X., Sun, J., Yan, B., “An approach to predict the residual stress and distortion during the selective laser melting of AlSi10Mg parts,” The International Journal of Advanced Manufacturing Technology, 97, pp. 3535-3546 (2018).CrossRefGoogle Scholar
Hill, M.R. and Nelson, D.V., The inherent strain method for residual stress determination and its application to a long welded joint, ASME-PUBLICATIONS-PVP, 318, pp. 343-352 (1995).Google Scholar
Wu, C.T., Bui, T.Q., Wu, Y., Luo, T.L., Wang, M., Liao, C.C., Chen, P.Y. and Lai, Y.S., “Numerical and experimental validation of a particle Galerkin method for metal grinding simulation,” Computational Mechanics, 61, pp. 365-383 (2018).CrossRefGoogle Scholar
Pan, X., Wu, C.T., Hu, W. and Wu, Y., “A momentum-consistent stabilization algorithm for Lagrangian particle methods in the thermos- mechanical friction drilling analysis,” Computational Mechanics, 64, pp. 625-644 (2019).CrossRefGoogle Scholar
Hallquist, J., LS-DYNA Users’ Manual, Livermore Software Technology Corporation, U.S.A. (2019).Google Scholar
Li, Y., Ma, L., Yang, Z., Guan, X., Nie, Y. and Yang, Z., “Statistical multiscale analysis of transient conduction and radiation heat transfer problem in random inhomogeneous porous materials,” CMES: Computer Modeling in Engineering & Sciences, 115, pp. 1-24 (2018).Google Scholar
Putar, F., Sorić, J., Lesičar, T. and Tonković, Z., “A Multiscale Method for Damage Analysis of Quasi- Brittle Heterogeneous Materials,” CMES: Computer Modeling in Engineering & Sciences, 120, pp. 123-156 (2019).CrossRefGoogle Scholar