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A Comparative Study of Layer Heating and Continuous Heating Methods on Prediction Accuracy of Residual Stresses in Selective Laser Melted Tube Samples

  • Thematic Section: Metal Additive Manufacturing Modeling Challenge Series 2020
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Abstract

Thermal distortion and residual stresses are two important factors that affect the quality and reliability of steel parts manufactured by laser powder bed fusion (LPBF) processes. A cost-effective model for evaluation of those heat effects is needed to refine the manufacturing process and provides insights into the product design and heat treatment. In this study, the layer heating method and sophisticated track-layer scanning method were applied to simulate the thermo-mechanical response of IN625 tube parts built by LPBF. Based on the similarity of temperature field in each layer deposit, a swept mesh was constructed to perform the thermal analysis for top layer, with the rest of layers referring to the temperature by node number offsetting. A novel explicit finite element analysis code accelerated by graphics processing unit was used for the massive-element numerical analysis. The computational accuracy and efficiency of the layer heating and track-layer scanning methods were compared in detail. It is shown that layer heating method can efficiently capture the pattern of stress distribution with reasonable accuracy in stress magnitude. The grouped track-layer scanning method can predict the residual stress and strain more accurately at a higher cost (5 ~ 10×). The elastic strain distribution was compared with the measurement by X-ray diffraction, confirming the accuracy of residual stress prediction.

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References

  1. Frazier WE (2014) Metal additive manufacturing: a review. J Mater Eng Perform 23(6):1917–1928

    Article  CAS  Google Scholar 

  2. DebRoy T, Wei HL, Zuback JS et al (2018) Additive manufacturing of metallic components–process, structure and properties. Prog Mater Sci 92:112–224

    Article  CAS  Google Scholar 

  3. du Plessis A, le Roux SG, Booysen G et al (2016) Directionality of cavities and porosity formation in powder-bed laser additive manufacturing of metal components investigated using X-ray tomography. 3D Print Addit Manuf 3(1):48–55

    Article  Google Scholar 

  4. King WE, Anderson AT, Ferencz RM et al (2015) Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges. Appl Phys Rev 2(4):041304

    Article  Google Scholar 

  5. Mukherjee T, Wei HL, De A, DebRoy T (2018) Heat and fluid flow in additive manufacturing–part II: powder bed fusion of stainless steel, and titanium, nickel and aluminum base alloys. Comput Mater Sci 150:369–380

    Article  CAS  Google Scholar 

  6. Santos LS, Gupta SK, Bruck HA (2018) Simulation of buckling of internal features during selective laser sintering of metals. Addit Manuf 23:235–245

    CAS  Google Scholar 

  7. Kayacan MY, Özsoy K, Duman B, Yilmaz N, Kayacan MC (2019) A study on elimination of failures resulting from layering and internal stresses in powder bed fusion (PBF) additive manufacturing. Mater Manuf Process 34(13):1467–1475

    Article  CAS  Google Scholar 

  8. Tran HT, Chen Q, Mohan J, To AC (2020) A new method for predicting cracking at the interface between solid and lattice support during laser powder bed fusion additive manufacturing. Addit Manuf 32:101050

    CAS  Google Scholar 

  9. Wei HL, Mukherjee T, Zhang W, Zuback JS, Knapp GL, De A, DebRoy T (2020) Mechanistic models for additive manufacturing of metallic components. Prog Mater Sci 19:100703

    Google Scholar 

  10. Matsumoto M, Shiomi M, Osakada K, Abe F (2002) Finite element analysis of single layer forming on metallic powder bed in rapid prototyping by selective laser processing. Int J Mach Tools Manuf 42(1):61–67

    Article  Google Scholar 

  11. Hussein A, Hao L, Yan C, Everson R (2013) Finite element simulation of the temperature and stress fields in single layers built without-support in selective laser melting. Mater Des 1980–2015(52):638–647

    Article  Google Scholar 

  12. Promoppatum P, Yao SC (2020) Influence of scanning length and energy input on residual stress reduction in metal additive manufacturing: numerical and experimental studies. J Manuf Process 49:247–259

    Article  Google Scholar 

  13. Lee YS, Zhang W (2016) Modeling of heat transfer, fluid flow and solidification microstructure of nickel-base superalloy fabricated by laser powder bed fusion. Addit Manuf 12:178–188

    CAS  Google Scholar 

  14. Chen F, Yan W (2020) High-fidelity modelling of thermal stress for additive manufacturing by linking thermal-fluid and mechanical models. Mater Des 196:109185

    Article  Google Scholar 

  15. Feng Z, Ma N, Li W, Narasaki K, Lu F (2020) Efficient analysis of welding thermal conduction using the Newton-Raphson method, implicit method, and their combination. Int J Adv Manuf Technol 111(7):1929–1940

    Article  Google Scholar 

  16. Ueda Y, Murakawa H, Ma N (2012) Welding deformation and residual stress prevention. Butterworth-Heinemann. https://doi.org/10.1016/C2011-0-06199-9

  17. Dunbar AJ, Denlinger ER, Gouge MF, Michaleris P (2016) Experimental validation of finite element modeling for laser powder bed fusion deformation. Addit Manuf 12:108–120

    Google Scholar 

  18. Chen Q, Liang X, Hayduke D, Liu J, Cheng L, Oskin J, Whitmore R, To AC (2019) An inherent strain based multiscale modeling framework for simulating part-scale residual deformation for direct metal laser sintering. Addit Manuf 28:406–418

    Google Scholar 

  19. Prabhakar P, Sames WJ, Dehoff R, Babu SS (2015) Computational modeling of residual stress formation during the electron beam melting process for Inconel 718. Addit Manuf 7:83–91

    CAS  Google Scholar 

  20. Yang YP, Jamshidinia M, Boulware P, Kelly SM (2018) Prediction of microstructure, residual stress, and deformation in laser powder bed fusion process. Comput Mech 61(5):599–615

    Article  Google Scholar 

  21. Xie R, Chen G, Zhao Y et al (2019) In-situ observation and numerical simulation on the transient strain and distortion prediction during additive manufacturing. J Manuf Process 38:494–501

    Article  Google Scholar 

  22. Ding J, Colegrove P, Mehnen J, Williams S, Wang F, Almeida PS (2014) A computationally efficient finite element model of wire and arc additive manufacture. Int J Adv Manuf Technol 70(1–4):227–236

    Article  Google Scholar 

  23. Denlinger ER, Gouge M, Irwin J, Michaleris P (2017) Thermomechanical model development and in situ experimental validation of the laser powder-bed fusion process. Addit Manuf 16:73–80

    Google Scholar 

  24. Huang H, Ma N, Chen J, Feng Z, Murakawa H (2020) Toward large-scale simulation of residual stress and distortion in wire and arc additive manufacturing. Addit Manuf 34:101248

    Google Scholar 

  25. Murakawa H, Ma N, Huang H (2015) Iterative substructure method employing concept of inherent strain for large-scale welding problems. Weld World 59(1):53–63. https://doi.org/10.1007/s40194-014-0178-z

    Article  Google Scholar 

  26. Huang H, Ma N, Murakawa H, Feng Z (2019) A dual-mesh method for efficient thermal stress analysis of large-scale welded structures. Int J Adv Manuf Technol 103(1–4):769–780

    Article  Google Scholar 

  27. Luo Z, Zhao Y (2020) Efficient thermal finite element modeling of selective laser melting of Inconel 718. Comput Mech 65(3):763–787

    Article  Google Scholar 

  28. Ikushima K, Shibahara M (2014) Prediction of residual stresses in multi-pass welded joint using idealized explicit FEM accelerated by a GPU. Comput Mat Sci 93:62–67. https://doi.org/10.1016/j.commatsci.2014.06.024

    Article  Google Scholar 

  29. Ma N (2016) An accelerated explicit method with GPU parallel computing for thermal stress and welding deformation of large structure models. Int J Adv Manuf Tech 87(5–8):2195–2211. https://doi.org/10.1007/s00170-016-8542-3

    Article  Google Scholar 

  30. Huang H, Chen J, Carlson B, Wang H-P, Crooker P, Frederick G, Feng Z (2018) Prediction of residual stresses in a multipass pipe weld by a novel 3D finite element approach. American society of mechanical engineers. In: ASME 2018 pressure vessels and piping conference (Paper No. PVP2018–85044, p V06BT06A084). https://doi.org/10.1115/pvp2018-85044

  31. Huang H, Wang Y, Chen J, Feng Z, Efficient numerical model for predicting residual stress and strain in parts manufactured by laser powder bed fusion. J Phys: Mater (under review)

  32. Huang H, Chen J, Feng Z, Wang H-P, Cai W, Carlson B (2021) Large-scale welding process simulation by GPU parallelized computing. Weld J (in press)

  33. Groeber M, Schwalbach E, Musinski W, Shade P, Donegan S, Uchic M, Sparkman D, Turner T, Miller J (2018) A preview of the US air force research laboratory additive manufacturing modeling challenge series. JOM 70(4):441–444

    Article  Google Scholar 

  34. Chuang AC, Park JS, Musinski JS, Shade PA et al (2020) Residual stresses formed in additively-manufactured Inconel 625 under a range of specimen configurations. Submitted to Integr Mater Manuf Innov (under review)

  35. Air Force Research Laboratory (AFRL) Additive Manufacturing (AM) Modeling Challenge Series. https://materials-data-facility.github.io/MID3AS-AM-Challenge/

  36. America Makes (2018) Project 4026: Development of Distortion Prediction and Compensation Methods for Metal Powder Bed Fusion Additive Manufacturing, Tech. Rep. America Makes Program sponsored by the Air Force Research Laboratory Under Agreement Number FA8650–12–2–7230

  37. Yang Y, Allen M, London T, Oancea V (2019) Residual strain predictions for a powder bed fusion Inconel 625 single cantilever part. Integr Mater Manuf Innov 8(3):294–304

    Article  Google Scholar 

  38. Huang H, Murakawa H (2015) Dynamic mesh refining and iterative substructure method for fillet welding thermo-mechanical analysis. Comput Model Eng Sci 106:187–201

    Google Scholar 

  39. Chen J, Zheng L, Feng Z, Zhang W, Dehoff RR (2013) Prediction of material thermal properties and beam-particle interaction at meso-scale during electron beam additive manufacturing. In: Materials science & technology conference, Montreal, Canada

  40. Goldak J, Chakravarti A, Bibby M (1984) A new finite element model for welding heat sources. Metall Trans B 15(2):299–305

    Article  Google Scholar 

Download references

Acknowledgements

This research was sponsored by the U.S. Department of Energy, Advanced Manufacturing Office, under a prime contract with Oak Ridge National Laboratory (ORNL). ORNL is managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract DE-AC05-00OR22725.

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Correspondence to Zhili Feng.

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Huang, H., Wang, Y., Chen, J. et al. A Comparative Study of Layer Heating and Continuous Heating Methods on Prediction Accuracy of Residual Stresses in Selective Laser Melted Tube Samples. Integr Mater Manuf Innov 10, 218–230 (2021). https://doi.org/10.1007/s40192-021-00217-4

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  • DOI: https://doi.org/10.1007/s40192-021-00217-4

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