Smart Structures and Systems

  • 제29권6호
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  • Pages.767-775
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  • 2022
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

In situ monitoring-based feature extraction for metal additive manufacturing products warpage prediction

  • Lee, Jungeon (Department of Industrial Engineering, Sungkyunkwan University) ;
  • Baek, Adrian M. Chung (Department of Mechanical Engineering, Ulsan National Institute of Science and Technology) ;
  • Kim, Namhun (Department of Mechanical Engineering, Ulsan National Institute of Science and Technology) ;
  • Kwon, Daeil (Department of Industrial Engineering, Sungkyunkwan University)
  • 투고 : 2021.10.31
  • 심사 : 2022.04.25
  • 발행 : 2022.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Metal additive manufacturing (AM), also known as metal three-dimensional (3D) printing, produces 3D metal products by repeatedly adding and solidifying metal materials layer by layer. During the metal AM process, products experience repeated local melting and cooling using a laser or electron beam, resulting in product defects, such as warpage, cracks, and internal pores. Such defects adversely affect the final product. This paper proposes the in situ monitoring-based warpage prediction of metal AM products with experimental feature extraction. The temperature profile of the metal AM substrate during the process was experimentally collected. Time-domain features were extracted from the temperature profile, and their relationships to the warpage mechanism were investigated. The standard deviation showed a significant linear correlation with warpage. The findings from this study are expected to contribute to optimizing process parameters for metal AM warpage reduction.

키워드

과제정보

This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020R1A4A407990411) and a Korea Institute for Advancement of Technology (KIAT) grant funded by the Korean government (MOTIE) (N0002429, The Competency Development Program for Industry Specialist).

참고문헌

  1. Arnold, C. and Korner, C. (2021), "In-situ electron optical measurement of thermal expansion in electron beam powder bed fusion", Add. Manuf., 46, 102213. https://doi.org/10.1016/j.addma.2021.102213.
  2. Bian, P., Shi, J., Liu, Y. and Xie, Y. (2020), "Influence of laser power and scanning strategy on residual stress distribution in additively manufactured 316L steel", Optic. Las. Technol., 132, 106477. https://doi.org/10.1016/j.optlastec.2020.106477.
  3. Caltanissetta, F., Grasso, M., Petro, S. and Colosimo, B.M. (2018), "Characterization of in-situ measurements based on layerwise imaging in laser powder bed fusion", Add. Manuf., 24, 183-199. https://doi.org/10.1016/j.addma.2018.09.017.
  4. Choi, T.Y. (2020), "Machine learning based predictive modeling of dimensional quality in direct energy deposition with SUS316L", Graduate School of UNIST.
  5. Dastjerdi, A.A., Movahhedy, M.R. and Akbari, J. (2017), "Optimization of process parameters for reducing warpage in selected laser sintering of polymer parts", Add. Manuf., 18, 285-294. https://doi.org/10.1016/j.addma.2017.10.018.
  6. Desai, P.D. and Ho, C.Y. (1978), "Thermal linear expansion of nine selected AISI stainless steels", Thermophysical and Electronic Properties Information Analysis Center Lafayette In.
  7. Foroozmehr, E. and Kovacevic, R. (2010), "Effect of path planning on the laser powder deposition process: Thermal and structural evaluation", Int. J. Adv. Manuf. Technol., 51(5), 659-669. https://doi.org/10.1007/s00170-010-2659-6.
  8. Frazier, W.E. (2014), "Metal additive manufacturing: A review", J. Mater. Eng. Perform., 23(6), 1917-1928. https://doi.org/10.1007/s11665-014-0958-z.
  9. Gere, J.M. and Goodno, B.J. (2009), Mechanics of Materials, Cengage Learning. Inc., Independence, KY.
  10. Khanzadeh, M., Chowdhury, S., Marufuzzaman, M., Tschopp, M.A. and Bian, L. (2018), "Porosity prediction: Supervised-learning of thermal history for direct laser deposition", J. Manuf. Syst., 47, 69-82. https://doi.org/10.1016/j.jmsy.2018.04.001.
  11. Kim, Y.W. and Jewong, W.B. (2020), "Defect classification of refrigerant compressor using variance estimation of the transfer function between pressure pulsation and shell acceleration", Smart Struct. Syst., 25(2), 255-264. https://doi.org/10.12989/sss.2020.25.2.255.
  12. Kumar, L.J. and Krishnadas Nair, C.G. (2017), "Current trends of additive manufacturing in the aerospace industry", Advances in 3D Printing & Additive Manufacturing Technologies, Springer, Singapore.
  13. Kyrsanidi, A.K., Kermanidis, T.B. and Pantelakis, S.G. (2000), "An analytical model for the prediction of distortions caused by the laser forming process", J. Mater. Proc. Technol., 104(1-2), 94-102. https://doi.org/10.1016/S0924-0136(00)00520-3.
  14. Lee, J. and Chung, H. (2020), "Experimental investigation of deposition pattern on the temperature and distortion of direct energy deposition-based additive manufactured part", Appl. Sci., 10(21), 7653. https://doi.org/10.3390/app10217653.
  15. Lee, Y., Lee, S., Zhao, X.G., Lee, D., Kim, T., Jung, H. and Kim, N. (2018), "Impact of UV curing process on mechanical properties and dimensional accuracies of digital light processing 3D printed objects", Smart Struct. Syst., 22(2), 161-166. https://doi.org/10.12989/sss.2018.22.2.161.
  16. Lewandowski, J.J. and Seifi, M. (2016), "Metal additive manufacturing: A review of mechanical properties", Ann. Rev. Mater. Res., 46, 151-186. https://doi.org/10.1146/annurevmatsci-070115-032024.
  17. Li, C., Liu, Z.Y., Fang, X.Y. and Guo, Y.B. (2018), "Residual stress in metal additive manufacturing", Procedia Cirp, 71, 348-353. https://doi.org/10.1016/j.procir.2018.05.039.
  18. Mageshwaran, G., Polisetti, S.R., Jeevahan, J. and Joseph, G.B. (2017), "Enhancement of uniform temperature distribution during casting solidification by methoding process", Int. J. Ambient Energy, 38(8), 774-780. https://doi.org/10.1080/01430750.2016.1222959.
  19. Matsunawa, A., Mizutani, M., Katayama, S. and Seto, N. (2003), "Porosity formation mechanism and its prevention in laser welding", Weld. Int., 17(6), 431-437. https://doi.org/10.1533/wint.2003.3138.
  20. Paul, R., Anand, S. and Gerner, F. (2014), "Effect of thermal deformation on part errors in metal powder based additive manufacturing processes", J. Manuf. Sci. Eng., 136(3), 031009. https://doi.org/10.1115/1.4026524.
  21. Rubino, F., Astarita, A. and Carlone, P. (2018), "Thermomechanical finite element modeling of the laser treatment of titanium cold-sprayed coatings", Coating., 8(6), 219. https://doi.org/10.3390/coatings8060219.
  22. Sanchez, R., Aisa, J., Martinez, A. and Mercado, D. (2012), "On the relationship between cooling setup and warpage in injection molding", Measure., 45(5), 1051-1056. https://doi.org/10.1016/j.measurement.2012.01.039.
  23. Shi, Y., Yao, Z., Shen, H. and Hu, J. (2006), "Research on the mechanisms of laser forming for the metal plate", Int. J. Mach. Tool. Manuf., 46(12-13), 1689-1697. https://doi.org/10.1016/j.ijmachtools.2005.09.016.
  24. Sim, J., Kim, S., Park, H.J. and Choi, J.H. (2020), "A tutorial for feature engineering in the prognostics and health management of gears and bearings", Appl. Sci., 10(16), 5639. https://doi.org/10.3390/app10165639.
  25. Srivastava, S., Garg, R.K., Sharma, V.S. and Sachdeva, A. (2021), "Measurement and mitigation of residual stress in wire-arc additive manufacturing: A review of macro-scale continuum modelling approach", Arch. Comput. Meth. Eng., 28(5), 3491-3515. https://doi.org/10.1007/s11831-020-09511-4.
  26. Stavropoulos, P. and Foteinopoulos, P. (2018), "Modelling of additive manufacturing processes: A review and classification", Manuf. Rev., 5, 2. https://doi.org/10.1051/mfreview/2017014.
  27. Vafadar, A., Guzzomi, F., Rassau, A. and Hayward, K. (2021), "Advances in metal additive manufacturing: A review of common processes, industrial applications, and current challenges", Appl. Sci., 11(3), 1213. https://doi.org/10.3390/app11031213.
  28. Venkatkumar, D. and Ravindran, D. (2016), "3D finite element simulation of temperature distribution, residual stress and distortion on 304 stainless steel plates using GTA welding", J. Mech. Sci. Technol., 30(1), 67-76. https://doi.org/10.1007/s12206-015-1208-5.
  29. Wang, H., Zhu, Q., Li, J., Mao, J., Hu, S. and Zhao, X. (2019), "Identification of moving train loads on railway bridge based on strain monitoring", Smart Struct. Syst., 23(3), 263-278. https://doi.org/10.12989/sss.2019.23.3.263.
  30. Zeng, C., Tian, W., Liao, W.H. and Hua, L. (2016), "Microstructure and porosity evaluation in laser-cladding deposited Ni-based coatings", Surf. Coating. Technol., 294, 122-130. https://doi.org/10.1016/j.surfcoat.2016.03.083.
  31. Zhang, Z., Liu, Z. and Wu, D. (2021), "Prediction of melt pool temperature in directed energy deposition using machine learning", Add. Manuf., 37, 101692. https://doi.org/10.1016/j.addma.2020.101692.