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Experimental Investigation on an Algorithm for Testing the Quality of Powder Distribution During 3D Printing Process

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Języki publikacji
EN
Abstrakty
EN
Metal 3D printing is a modern manufacturing process that allows the production of geometrically complex structures from metallic powders of varying chemical composition. This paper shows the results of testing the powder feeding and distribution system of the developed 3D printer. The device using the SLM method (Selected Laser Melting) was developed by research team of WroclawTech and used in this investigation. The powder feeding and distribution system was tested using a vision system integrated into the printer control system. Thousands of tests performed made it possible to identify the reasons corresponding to incorrect powder distribution on the working field. In addition, a quality control algorithm was developed and implemented in the MatLab environment. Algorithms based on image analysis automatically identifies powder distributed in an unacceptable way. An 88% accuracy rate was achieved for identifying defects in all images within a dataset of 600 pictures, classified into following categories OK and NOK consisting of: recoater streaking, recoater hopping, super-elevation. The strength of the algorithm developed lies in its utilization of variations in shades of gray, rather than solely relying on the actual gray values. This approach grants the algorithm a certain degree of adaptability to changing lighting conditions.
Twórcy
  • Faculty of Mechanical Engineering Welding Division, Department of Metal Forming, Welding and Metrology, Wroclaw University of Science and Technology, ul. Lukasiewicza 5, 50-371 Wroclaw, Poland
  • Faculty of Mechanical Engineering Welding Division, Department of Metal Forming, Welding and Metrology, Wroclaw University of Science and Technology, ul. Lukasiewicza 5, 50-371 Wroclaw, Poland
  • Faculty of Mechanical Engineering Welding Division, Department of Metal Forming, Welding and Metrology, Wroclaw University of Science and Technology, ul. Lukasiewicza 5, 50-371 Wroclaw, Poland
  • Faculty of Mechanical Engineering Welding Division, Department of Metal Forming, Welding and Metrology, Wroclaw University of Science and Technology, ul. Lukasiewicza 5, 50-371 Wroclaw, Poland
  • Faculty of Mechanical Engineering Welding Division, Department of Metal Forming, Welding and Metrology, Wroclaw University of Science and Technology, ul. Lukasiewicza 5, 50-371 Wroclaw, Poland
  • Faculty of Mechanical Engineering Welding Division, Department of Metal Forming, Welding and Metrology, Wroclaw University of Science and Technology, ul. Lukasiewicza 5, 50-371 Wroclaw, Poland
Bibliografia
  • 1. S. Gade, S. Vagge, M. Rathod, A Review on Additive Manufacturing – Methods, Materials, and its Associated Failures, Adv. Sci. Technol. Res. J. 17 (2023)b40–63. https://doi.org/10.12913/22998624/163001.
  • 2. A. Terelak-Tymczyna, E. Bachtiak-Radka, D. Grzesiak, A. Jardzioch, Comparison of the Classic and Hybrid Production Methods with the Use of SLM Taking into Account the Aspects of Sustainable Production Development, Adv. Sci. Technol. Res. J. 17 (2023) 94–107. https://doi. org/10.12913/22998624/156916.
  • 3. C. Meier, R. Weissbach, J. Weinberg, W.A. Wall, A.J. Hart, Critical influences of particle size and adhesion on the powder layer uniformity in metal additive manufacturing, Journal of Materials Pro-cessing Technology 266 (2019) 484–501. https:// doi.org/10.1016/j.jmatprotec.2018.10.037.
  • 4. M.A. Buhairi, F.M. Foudzi, F.I. Jamhari, A.B. Sulong, N.A.M. Radzuan, N. Muhamad, I.F. Mohamed, A.H. Azman, W.S.W. Harun, M.S.H. Al- Furjan, Review on volumetric energy density: influence on morphology and mechanical properties of Ti6Al4V manufactured via laser powder bed fusion, Prog Addit Manuf 8 (2023) 265–283. https://doi. org/10.1007/s40964-022-00328-0.
  • 5. T.-P. Le, X. Wang, K.P. Davidson, J.E. Fronda, M Seita, Experimental analysis of powder layer quality as a function of feedstock and recoating strategies, Additive Manufacturing 39 (2021) #101890. https:// doi.org/10.1016/j.addma.2021.101890.
  • 6. P. Avrampos, G.-C. Vosniakos, A review of powder deposition in additive manufacturing by powder bed fusion, Journal of Manufacturing Processes 74 (2022) 332–352. https://doi.org/10.1016/j. jmapro.2021.12.021.
  • 7. D. Yao, X. An, H. Fu, H. Zhang, X. Yang, Q. Zou, K. Dong, Dynamic investigation on the powder spreading during selective laser melting additive manufacturing, Additive Manufacturing 37 (2021) #101707. https://doi.org/10.1016/j.addma.2020.101707.
  • 8. X. Cui, S. Zhang, C.H. Zhang, J. Chen, J.B. Zhang, S.Y. Dong, Additive manufacturing of 24CrNiMo low alloy steel by selective laser melting: Influence of volumetric energy density on densification, microstructure and hardness, Materials Science and Engineering: A 809 (2021) #140957. https://doi. org/10.1016/j.msea.2021.140957.
  • 9. D. Oropeza, R. Roberts, A.J. Hart, A modular testbed for mechanized spreading of powder layers for additive manufacturing, Rev. Sci. Instrum. 92 (2021) 15114. https://doi.org/10.1063/5.0031191.
  • 10. B. Nagarajan, Z. Hu, X. Song, W. Zhai, J. Wei, Development of Micro Selective Laser Melting: The State of the Art and Future Perspectives, Engineering 5 (2019) 702–720. https://doi.org/10.1016/j. eng.2019.07.002.
  • 11. H.-Y. Chen, C.-C. Lin, M.-H. Horng, L.-K. Chang, J.-H. Hsu, T.-W. Chang, J.-C. Hung, R.-M. Lee, M.- C. Tsai, Deep Learning Applied to Defect Detection in Powder Spreading Process of Magnetic Material Additive Manufacturing, Materials (Basel) 15 (2022). https://doi.org/10.3390/ma15165662.
  • 12. L. Scime, D. Siddel, S. Baird, V. Paquit, Layerwise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel- wise semantic segmentation, Additive Manufacturing 36 (2020) #101453. https://doi.org/10.1016/j. addma.2020.101453.
  • 13. C. Wang, X.P. Tan, S.B. Tor, C.S. Lim, Machine learning in additive manufacturing: State-of- the-art and perspectives, Additive Manufacturing 36 (2020) #101538. https://doi.org/10.1016/j. addma.2020.101538.
  • 14. Y. Yin, Liming, G. Dali, Research on Feature Extraction of Local Binary Pattern of SLM Powder Bed Gray Image, J. Phys.: Conf. Ser. 1885 (2021) #32007. https://doi.org/10.1088/1742-6596/1885/3/032007.
  • 15. L. Scime, J. Beuth, Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm, Additive Manufacturing 19 (2018) 114–126. https:// doi.org/10.1016/j.addma.2017.11.009.
  • 16. J. Liu, J. Ye, D. Silva Izquierdo, A. Vinel, N. Shamsaei, S. Shao, A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing, J Intell Manuf (2022). https://doi.org/10.1007/ s10845-022-02012-0.
  • 17. T. Craeghs, S. Clijsters, E. Yasa, J. Kruth, Online quality control of selective laser melting, In Proceedings of the 20th Solid Freeform Fabrication (SFF) Symposium Austin, TX, USA, 8–10 August 2011.
  • 18. J.Z. Jacobsmuhlen, S. Kleszczynski, G. Witt, Merhof, Detection of elevated regions in surface images from laser beam melting processes, In: Proceedings of the IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society Yokohama, Japan, 9–12 November 2015; 1270–1275.
  • 19. A.J. Dunbar, E.R. Denlinger, J. Heigel, P. Michaleris, P. Guerrier, R. Martukanitz, T.W. Simpson, Development of experimental method for in situ distortion and temperature measurements during the laser powder bed fusion additive manufacturing process, Additive Manufacturing 12 (2016) 25–30. https:// doi.org/10.1016/j.addma.2016.04.007.
  • 20. MATLAB documention, The MathWorks Inc. (2021). MATLAB version R2021b, Natick, Masachusetts: The MathWorks Inc. https://www.math- works.com
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-35920f61-0a3d-4a4f-a79c-0d9b193fb3c4
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