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Development of multi-defect diagnosis algorithm for the directed energy deposition (DED) process with in situ melt-pool monitoring

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Abstract

The directed energy deposition (DED) process is attracting significant attention in high value-added industries, such as automobiles and aviation, because the process can freely manufacture components with complex shapes and directly stack them on metal substrates. However, it has a problem of degradation in reliability and poor reproducibility due to the influence of various parameters present in the process, and various defects are likely to occur inside and outside the product. To solve this problem, a proper data-driven prognostics and health management (PHM) approach is required. Therefore, this study proposes a multi-defect diagnosis algorithm for the DED process based on in situ melt-pool monitoring. First, the DED process monitoring testbed using a CCD camera and a pyrometer was established. The image pre-processing algorithms are developed for the effective extraction of region-of-interest (ROI) areas of the melt-pool and for effective quantification of internal defects, such as pores. Then, critical features of the melt-pool that are closely related to various defects—melting balls, low pores, and high pores—are extracted. Finally, the multi-defect diagnosis algorithm combining several binary classification models is developed, and it is demonstrated that support vector machine (SVM) showed the best performance, with an average accuracy of 92.7%.

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References

  1. Guo N, Leu MC (2013) Additive manufacturing: technology, applications and research need. Front Mech Eng 8(3):215–243. https://doi.org/10.1007/s11465-013-0248-8

    Article  Google Scholar 

  2. Wong V, Hernandez A (2012) A review of additive manufacturing. Int Sch Res Notices. https://doi.org/10.5402/2012/208760

    Article  Google Scholar 

  3. Frazier WE (2014) Metal additive manufacturing: a review. J Mater Eng Perform 23:1917–1928. https://doi.org/10.1007/s11665-014-0958-z

    Article  Google Scholar 

  4. Dutta B, Palaniswamy S, Choi J, Song L, Mazumder J (2011) Additive manufacturing by direct metal deposition. Adv Mater Processes 169(5):33–36

    Google Scholar 

  5. Saboori A, Aversa A, Marchese G, Biamino S, Lombardi M, Fino P (2019) Application of directed energy deposition-based additive manufacturing in repair. Appl Sci 9(16):3316. https://doi.org/10.3390/app9163316

    Article  Google Scholar 

  6. Dass A, Moridi A (2019) State of the art in directed energy deposition: from additive manufacturing to materials design. Coatings 9(7):418. https://doi.org/10.3390/coatings9070418

    Article  Google Scholar 

  7. Yang Y, Gong Y, Li C, Wen X, Sun J (2021) Mechanical performance of 316 L stainless steel by hybrid directed energy deposition and thermal milling process. J Mater Process Tech 291:117023. https://doi.org/10.1016/j.jmatprotec.2020.117023

  8. Yang Y, Gong Y, Qu S, Xie H, Cai M, Xu Y (2020) Densification, mechanical behaviors, and machining characteristics of 316L stainless steel in hybrid additive/subtractive manufacturing. Int J Adv Manuf Technol 107:177–189. https://doi.org/10.1007/s00170-020-05033-2

    Article  Google Scholar 

  9. Qu S (2021) Gong Y (2021) Effect of heat treatment on microstructure and mechanical characteristics of 316L stainless steel parts fabricated by hybrid additive and subtractive process. Int J Adv Manuf Technol 117:3465–3475. https://doi.org/10.1007/s00170-021-07786-w

    Article  Google Scholar 

  10. Gong Y, Yang Y, Qu S, Li P, Liang C, Zhang H (2019) Laser energy density dependence of performance in additive/subtractive hybrid manufacturing of 316L stainless steel. Int J Adv Manuf Technol 105:1585–1596. https://doi.org/10.1007/s00170-019-04372-z

    Article  Google Scholar 

  11. Yang Y, Gong Y, Qu S, Rong Y, Sun Y, Cai M (2018) Densification, surface morphology, microstructure and mechanical properties of 316L fabricated by hybrid manufacturing. Int J Adv Manuf Technol 97:2687–2696. https://doi.org/10.1007/s00170-018-2144-1

    Article  Google Scholar 

  12. Yang Y, Gong Y, Qu S, Yin G, Liang C, Li P (2021) Additive and subtractive hybrid manufacturing (ASHM) of 316L stainless steel: single-track specimens, microstructure, and mechanical properties. JOM 73:759–769. https://doi.org/10.1007/s11837-020-04216-2

    Article  Google Scholar 

  13. Kistler NA, Nassar AR, Reutzel EW, Corbin DC, Beese AM (2017) Effect of directed energy deposition processing parameters on laser deposited Inconel® 718: microstructure, fusion zone morphology, and hardness. J Laser Appl 29(2):022005. https://doi.org/10.2351/1.4979702

  14. Shamsaei N, Yadollahi A, Bian L, Thompson SM (2015) An overview of direct laser deposition for additive manufacturing; Part II: Mechanical behavior, process parameter optimization and control. Addit Manuf 8:12–35. https://doi.org/10.1016/j.addma.2015.07.002

    Article  Google Scholar 

  15. Kim JS, Kang BJ, Lee SW (2019) An experimental study on microstructural characteristics and mechanical properties of stainless-steel 316L parts using directed energy deposition (DED) process. J Mech Sci Technol 33(12):5731–5737. https://doi.org/10.1007/s12206-019-1116-1

    Article  Google Scholar 

  16. Nursyifaulkhair D, Park N, Baek ER, Lee JB (2019) Effect of process parameters on the formation of lack of fusion in directed energy deposition of Ti-6Al-4V alloy. J Weld Join 37(6):579–584. https://doi.org/10.5781/JWJ.2019.37.6.7

    Article  Google Scholar 

  17. Fujishima M, Oda Y, Ashida R, Takezawa K, Kondo M (2017) Study on factors for pores and cladding shape in the deposition processes of Inconel 625 by the directed energy deposition (DED) method. CIRP J Manuf Sci Technol 19:200–204. https://doi.org/10.1016/j.cirpj.2017.04.003

    Article  Google Scholar 

  18. Taheri H, Koester LW, Bigelow TA, Faierson EJ, Bond LJ (2019) In situ additive manufacturing process monitoring with an acoustic technique: clustering performance evaluation using K-means algorithm. J Manuf Sci Eng 141(4):041011. https://doi.org/10.1115/1.4042786

  19. Gaja H, Liou F (2017) Defects monitoring of laser metal deposition using acoustic emission sensor. Int J Adv Manuf Technol 90(1):561–574. https://doi.org/10.1007/s00170-016-9366-x

    Article  Google Scholar 

  20. Wang L, Felicelli SD, Craig JE (2007) Thermal modeling and experimental validation in the LENS™ process, In 2007 International Solid Freeform Fabrication Symposium. https://doi.org/10.26153/tsw/7197

  21. Bi G, Gasser A, Wissenbach K, Drenker A, Poprawe R (2006) Investigation on the direct laser metallic powder deposition process via temperature measurement. Appl Surf Sci 253(3):1411–1416. https://doi.org/10.1016/j.apsusc.2006.02.025

    Article  Google Scholar 

  22. Khanzadeh M, Chowdhury S, Tschopp MA, Doude HR, Marufuzzaman M, Bian L (2019) In situ monitoring of melt pool images for porosity prediction in directed energy deposition processes. IISE Trans 51(5):437–455. https://doi.org/10.1080/24725854.2017.1417656

    Article  Google Scholar 

  23. Seifi SH, Tian W, Doude H, Tschopp MA, Bian L (2018) Layer-wise profile monitoring of laser-based additive manufacturing, In 2018 International Solid Freeform Fabrication Symposium. https://doi.org/10.26153/tsw/17014

  24. Montazeri M, Nassar AR, Stutzman CB, Rao P (2019) Heterogeneous sensor-based condition monitoring in directed energy deposition. Addit Manuf 30:100916. https://doi.org/10.1016/j.addma.2019.100916

  25. Sames WJ, List FA, Pannala S, Dehoff RR, Babu SS (2016) The metallurgy and processing science of metal additive manufacturing. Int Mater Rev 61(5):315–360. https://doi.org/10.1080/09506608.2015.1116649

    Article  Google Scholar 

  26. Gibson I, Rosen DW, Stucker B (2015) Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing, 2nd edn. Springer, New York:245–268. https://doi.org/10.1007/978-1-4939-2113-3

  27. Dilip JJS, Zhang S, Teng C, Zeng K, Robinson C, Pal D, Stucker B (2017) Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in Ti-6Al-4V alloy parts fabricated by selective laser melting. Prog Addit Manuf 2(3):157–167. https://doi.org/10.1007/s40964-017-0030-2

    Article  Google Scholar 

  28. Kim JS (2020) Comprehensive characterization of directed energy deposition (DED) process, PhD thiesis, Sungkyunkwan University

  29. Park JM, Jeon JM, Kim JG, Seong Y, Park SH, Kim HS (2018) Effect of porosity on mechanical anisotropy of 316L austenitic stainless steel additively manufactured by selective laser melting. J Korean Powder Metall Inst 25(6):475–481. https://doi.org/10.4150/KPMI.2018.25.6.475

    Article  Google Scholar 

  30. Gu D, Shen Y (2009) Balling phenomena in direct laser sintering of stainless steel powder: metallurgical mechanisms and control methods. Mater Des 30(8):2903–2910. https://doi.org/10.1016/j.matdes.2009.01.013

    Article  Google Scholar 

  31. Vilaro T, Colin C, Bartout JD (2011) As-fabricated and heat-treated microstructures of the Ti-6Al-4V alloy processed by selective laser melting. Metall and Mater Trans A 42(10):3190–3199. https://doi.org/10.1007/s11661-011-0731-y

    Article  Google Scholar 

  32. Hofman JT, Pathiraj B, Van Dijk J, De Lange DF, Meijer J (2012) A camera based feedback control strategy for the laser cladding process. J Mater Process Technol 212(11):2455–2462. https://doi.org/10.1016/j.jmatprotec.2012.06.027

    Article  Google Scholar 

Download references

Funding

This research was supported by the Ministry of Science, ICT (MSIT), Korea, under the High-Potential Individuals Global Training Program (2021–0-01550), supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP), and the National Research Foundation of Korea (NRF) grant (2022R1A2C3012900), funded by the Korea government (MSIT).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Graduate School, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea

    Hyewon Shin

  2. Data Intelligence Team, Innovation Center, Samsung Electronics Company, Hwaseong-si, Gyeonggi-do, 18399, Republic of Korea

    Jimin Lee

  3. G. W. W. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Seung-Kyum Choi

  4. School of Mechanical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea

    Sang Won Lee

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  1. Hyewon Shin
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  2. Jimin Lee
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  3. Seung-Kyum Choi
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  4. Sang Won Lee
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Contributions

HS, experiments, analysis, writing—original draft; JL, experiments, methodology, and modeling; S-KC, supervision; SWL, conceptualization, supervision, and writing (review and editing).

Corresponding author

Correspondence to Sang Won Lee.

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Hyewon Shin and Jimin Lee are co-first authors.

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Shin, H., Lee, J., Choi, SK. et al. Development of multi-defect diagnosis algorithm for the directed energy deposition (DED) process with in situ melt-pool monitoring. Int J Adv Manuf Technol 125, 357–368 (2023). https://doi.org/10.1007/s00170-022-10711-4

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