Abstract
Among metal additive manufacturing (AM) techniques, directed energy deposition (DED) has the advantage of being able to stack various materials, including difficult-to-cut materials such as Inconel 718. However, the pores generated during the DED process may reduce the hardness or abrasion resistance of the product and cause cracks or serious damage to the product. In this study, a method for detecting and quantifying pores using machine learning to detect pores in Inconel 718 products fabricated by DED was proposed. A pore detection model with YOLOv5 was established to detect and quantify porosity by learning 2448 pore images of Inconel 718 fabricated by DED. To minimize the porosity of Inconel 718 fabricated by DED, the effect of the DED process parameters on the porosity was analyzed using the design of experiments (DOE). Using the DOE, a prediction equation for predicting porosity was established, and the optimal conditions were obtained. The product was manufactured under optimal conditions, and porosity verification experiments and hardness test were performed. The resulting trends of the predicted values and the experimental results were consistent, and the hardness increased by 6.73% in the workpiece fabricated by DED compared to the workpiece fabricated by casting.
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References
Johnson NS, Vulimiri PS, To AC et al (2020) Invited review: machine learning for materials developments in metals additive manufacturing. Addit Manuf 36. https://doi.org/10.1016/j.addma.2020.101641
Jiang J, Xiong Y, Zhang Z, Rosen DW (2020) Machine learning integrated design for additive manufacturing. J Intell Manuf. https://doi.org/10.1007/s10845-020-01715-6
Aboulkhair NT, Everitt NM, Ashcroft I, Tuck C (2014) Reducing porosity in AlSi10Mg parts processed by selective laser melting. Addit Manuf 1:77–86. https://doi.org/10.1016/j.addma.2014.08.001
Frazier WE (2014) Metal additive manufacturing: a review. J Mater Eng Perform 23:1917–1928. https://doi.org/10.1007/s11665-014-0958-z
Woo WS, Kim EJ, Jeong HI, Lee CM (2020) Laser-assisted machining of Ti-6Al-4V fabricated by DED additive manufacturing. Int J Precis Eng Manuf - Green Technol 7:559–572. https://doi.org/10.1007/s40684-020-00221-7
Durão LFCS, Barkoczy R, Zancul E et al (2019) Optimizing additive manufacturing parameters for the fused deposition modeling technology using a design of experiments. Prog Addit Manuf 4:291–313. https://doi.org/10.1007/s40964-019-00075-9
Kuriya T, Koike R, Mori T, Kakinuma Y (2018) Relationship between solidification time and porosity with directed energy deposition of Inconel 718. J Adv Mech Des Syst Manuf 12:1–11. https://doi.org/10.1299/jamdsm.2018jamdsm0104
Koike R, Misawa T, Aoyama T, Kondo M (2018) Controlling metal structure with remelting process in direct energy deposition of Inconel 625. CIRP Ann 67:237–240. https://doi.org/10.1016/j.cirp.2018.04.061
Khanna N, Zadafiya K, Patel T et al (2021) Review on machining of additively manufactured nickel and titanium alloys. J Mater Res Technol 15:3192–3221. https://doi.org/10.1016/j.jmrt.2021.09.088
Thomas M, Baxter GJ, Todd I (2016) Normalised model-based processing diagrams for additive layer manufacture of engineering alloys. Acta Mater 108:26–35. https://doi.org/10.1016/j.actamat.2016.02.025
Onuike B, Bandyopadhyay A (2019) Additive manufacturing in repair: influence of processing parameters on properties of Inconel 718. Mater Lett 252:256–259. https://doi.org/10.1016/j.matlet.2019.05.114
Huang Y, Yuan Y, Yang L et al (2020) A study on porosity in gas tungsten arc welded aluminum alloys using spectral analysis. J Manuf Process 57:334–343. https://doi.org/10.1016/J.JMAPRO.2020.06.033
Caggiano A, Zhang J, Alfieri V et al (2019) Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Ann 68:451–454. https://doi.org/10.1016/j.cirp.2019.03.021
Wang C, Tan XP, Tor SB, Lim CS (2020) Machine learning in additive manufacturing: state-of-the-art and perspectives. Addit Manuf 36:101538
Qi X, Chen G, Li Y et al (2019) Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering 5:721–729. https://doi.org/10.1016/j.eng.2019.04.012
Kim DH, Kim TJY, Wang X et al (2018) Smart machining process using machine learning: a review and perspective on machining industry. Int J Precis Eng Manuf - Green Technol 5:555–568. https://doi.org/10.1007/s40684-018-0057-y
Nasiri S, Khosravani MR (2021) Machine learning in predicting mechanical behavior of additively manufactured parts. J Mater Res Technol 14:1137–1153. https://doi.org/10.1016/j.jmrt.2021.07.004
Stathatos E, Vosniakos GC (2019) Real-time simulation for long paths in laser-based additive manufacturing: a machine learning approach. Int J Adv Manuf Technol 104:1967–1984. https://doi.org/10.1007/s00170-019-04004-6
Meng L, McWilliams B, Jarosinski W et al (2020) Machine learning in additive manufacturing: a review. Jom 72:2363–2377. https://doi.org/10.1007/s11837-020-04155-y
Džugan J, Halmešová K, Ackermann M et al (2020) Thermo-physical properties investigation in relation to deposition orientation for SLM deposited H13 steel. Thermochim Acta 683:178479. https://doi.org/10.1016/J.TCA.2019.178479
Arévalo C, Ariza E, Pérez-Soriano EM et al (2020) Effect of processing atmosphere and secondary operations on the mechanical properties of additive manufactured AISI 316L stainless steel by plasma metal deposition. Metals (Basel) 10:1125. https://doi.org/10.3390/met10091125
Hosseini E, Popovich VA (2019) A review of mechanical properties of additively manufactured Inconel 718. Addit Manuf 30:100877. https://doi.org/10.1016/j.addma.2019.100877
Tan Zhi’En E, Pang JHL, Kaminski J, (2021) Directed energy deposition build process control effects on microstructure and tensile failure behaviour. J Mater Process Technol 294:117139. https://doi.org/10.1016/j.jmatprotec.2021.117139
Svetlizky D, Zheng B, Steinberg DM et al (2022) The influence of laser directed energy deposition (DED) processing parameters for Al5083 studied by central composite design. J Mater Res Technol 17:3157–3171. https://doi.org/10.1016/j.jmrt.2022.02.042
Dai W, Li D, Tang D et al (2021) Deep learning assisted vision inspection of resistance spot welds. J Manuf Process 62:262–274. https://doi.org/10.1016/j.jmapro.2020.12.015
Kumar R (2020) Modified mix design and statistical modelling of lightweight concrete with high volume micro fines waste additive via the Box-Behnken design approach. Cem Concr Compos 113:103706. https://doi.org/10.1016/j.cemconcomp.2020.103706
Asadzadeh S, Khoshbayan S (2018) Multi-objective optimization of influential factors on production process of foamed concrete using Box-Behnken approach. Constr Build Mater 170:101–110. https://doi.org/10.1016/j.conbuildmat.2018.02.189
Sola A, Nouri A (2019) Microstructural porosity in additive manufacturing: the formation and detection of pores in metal parts fabricated by powder bed fusion. J Adv Manuf Process 1:1–21. https://doi.org/10.1002/amp2.10021
Kaynak Y, Kitay O (2019) The effect of post-processing operations on surface characteristics of 316L stainless steel produced by selective laser melting. Addit Manuf 26:84–93. https://doi.org/10.1016/j.addma.2018.12.021
Kim E-J, Lee C-M, Kim D-H (2021) The effect of post-processing operations on mechanical characteristics of 304L stainless steel fabricated using laser additive manufacturing. J Mater Res Technol 15:1370–1381. https://doi.org/10.1016/j.jmrt.2021.08.142
ISO 6892–1: (2016) Metallic materials-tensile testing-Part 1: Method of test at room temperature. International Orgainzation for Standardization, Genea
Yang Z, Zhu L, Ning J et al (2022) Revealing the influence of ultrasound/heat treatment on microstructure evolution and tensile failure behavior in 3D-printing of Inconel 718. J Mater Process Technol 305:117574. https://doi.org/10.1016/j.jmatprotec.2022.117574
Popovich VA, Borisov EV, Popovich AA et al (2017) Impact of heat treatment on mechanical behaviour of Inconel 718 processed with tailored microstructure by selective laser melting. Mater Des 131:12–22. https://doi.org/10.1016/j.matdes.2017.05.065
Funding
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1A2B5B03001884, No. 2019R1A5A8083201).
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Jae-Hyun Kim: experiment analyses, measurements, and writing—original draft preparation. Won-Jung Oh: conceptualization, methodology, and experiments. Choon-Man Lee and Dong-Hyeon Kim: writing-reviewing, editing, and supervision.
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Kim, JH., Oh, WJ., Lee, CM. et al. Achieving optimal process design for minimizing porosity in additive manufacturing of Inconel 718 using a deep learning-based pore detection approach. Int J Adv Manuf Technol 121, 2115–2134 (2022). https://doi.org/10.1007/s00170-022-09372-0
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DOI: https://doi.org/10.1007/s00170-022-09372-0