Acta Mechanica Slovaca 2021, 25(2):6-13 | DOI: 10.21496/ams.2021.015

Metallurgical and Geometric Properties Controlling of Additively Manufactured Products using Artificial Intelligence

Snehashis Pal1, *, Igor Drstvenšek1
1 Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia

This article has presented a technical concept for producing precisely desired Additive Manufactured (AM) metallic products using Artificial Intelligence (AI). Due to the stochastic nature of the metallic AM process, which causes a greater variance in product properties compared to traditional manufacturing processes, significant inaccuracies in metallurgical properties, as well as geometry, occur. The physics behind these phenomena are related to the melting process, bonding, cooling rate, shrinkage, support condition, part orientation. However, by controlling these phenomena, a wide range of product features can be achieved using the fabricating parameters. A variety of fabricating parameters are involved in the metal AM process, but an appropriate combination of these parameters for a given material is required to obtain an accurate and desired product. Zero defect product can be achieved by controlling these parameters by implementing Knowledge-Based System (KBS). A suitable combination of manufacturing parameters can be determined using mathematical tools with AI, considering the manufacturing time and cost. The knowledge required to integrate AM manufacturing characteristics and constraints into the design and fabricating process is beyond the capabilities of any single engineer. Concurrent Engineering enables the integration of design and manufacturing to enable trades based not only on product performance, but also on other criteria that are not easily evaluated, such as production capability and support. A decision support system or KBS that can guide manufacturing issues during the preliminary design process would be an invaluable tool for system designers. The main objective of this paper is to clearly describe the metal AM manufacturing process problem and show how to develop a KBS for manufacturing process determination.

Keywords: metallurgical properties; geometry; additive manufacturing; artificial intelligence; knowledge-based system.

Received: March 14, 2021; Revised: April 6, 2021; Accepted: April 11, 2021; Published: June 25, 2021  Show citation

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Pal, S., & Drstvenšek, I. (2021). Metallurgical and Geometric Properties Controlling of Additively Manufactured Products using Artificial Intelligence. Acta Mechanica Slovaca, 25(2), 6-13. doi: 10.21496/ams.2021.015
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    References

    1. Gao, W.; Zhang, Y.; Ramanujan, D.; Ramani, K.; Chen, Y.; Williams, C.B.; Wang, C.C.L.; Shin, Y.C.; Zhang, S.; Zavattieri, P.D. The status, challenges, and future of additive manufacturing in engineering. Computer-Aided Design 2015, 69, 65-89. Go to original source...
    2. Thompson, S.M.; Bian, L.; Shamsaei, N.; Yadollahi, A. An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics. Additive Manufacturing 2015, 8, 36-62. Go to original source...
    3. Sun, J.; Yang, Y.; Wang, D. Parametric optimization of selective laser melting for forming Ti6Al4V samples by Taguchi method. Optics and Laser Technology 2013, 49, 118-124. Go to original source...
    4. Bogers, M.; Hadar, R.; Bilberg, A. Additive manufacturing for consumer-centric business models: Implications for supply chains in consumer goods manufacturing. Technological Forecasting and Social Change 2016, 102, 225-239. Go to original source...
    5. In'T Veld, B.H.; Overmeyer, L.; Schmidt, M.; Wegener, K.; Malshe, A.; Bartolo, P. Micro additive manufacturing using ultra short laser pulses. CIRP Annals - Manufacturing Technology 2015.
    6. Zhao, X.; Song, B.; Zhang, Y.; Zhu, X.; Wei, Q.; Shi, Y. Decarburization of stainless steel during selective laser melting and its influence on Young's modulus, hardness and tensile strength. Materials Science and Engineering: A 2015, 647, 58-61. Go to original source...
    7. Hussein, A.; Hao, L.; Yan, C.; Everson, R. Finite element simulation of the temperature and stress fields in single layers built without-support in selective laser melting. Materials & Design (1980-2015) 2013, 52, 638-647. Go to original source...
    8. Vilaro, T.; Colin, C.; Bartout, J.D.; Nazé, L.; Sennour, M. Microstructural and mechanical approaches of the selective laser melting process applied to a nickel-base superalloy.Materials Science and Engineering A 2012, 534, 446-451. Go to original source...
    9. Thijs, L.; Kempen, K.; Kruth, J.-P.; Van Humbeeck, J. Fine-structured aluminium products with controllable texture by selective laser melting of pre-alloyed AlSi10Mg powder. Acta Materialia 2013, 61, 1809-1819. Go to original source...
    10. Pal, S.; Gubeljak, N.; Bončina, T.; Hudák, R.; Toth, T.; Zivcak, J.; Lojen, G.; Leben, N.; Drstvenšek, I. The effects of locations on the build tray on the quality of specimens in powder bed additive manufacturing. International Journal of Advanced Manufacturing Technology 2021, 112, 1159-1170. Go to original source...
    11. Pal, S.; Lojen, G.; Gubeljak, N.; Kokol, V.; Drstvensek, I. Melting, fusion and solidification behaviors of Ti-6Al-4V alloy in selective laser melting at different scanning speeds. Rapid Prototyping Journal 2020, 26, 1209-1215. Go to original source...
    12. Pal, S.; Kokol, V.; Gubeljak, N.; Hadzistevic, M.; Hudak, R.; Drstvensek, I. Dimensional errors in selective laser melting products related to different orientations and processing parameters. Materiali in tehnologije 2019, 53, 551-558. Go to original source...
    13. Ma, M.; Wang, Z.; Gao, M.; Zeng, X. Layer thickness dependence of performance in high-power selective laser melting of 1Cr18Ni9Ti stainless steel. Journal of Materials Processing Technology 2015, 215, 142-150. Go to original source...
    14. Nakamoto, T.; Shirakawa, N.; Miyata, Y.; Inui, H. Selective laser sintering of high carbon steel powders studied as a function of carbon content. Journal of Materials Processing Technology 2009, 209, 5653-5660. Go to original source...
    15. Harrison, N.J.; Todd, I.; Mumtaz, K. Reduction of micro-cracking in nickel superalloys processed by Selective Laser Melting: A fundamental alloy design approach. Acta Materialia 2015, 94, 59-68. Go to original source...
    16. Olakanmi, E.O.; Cochrane, R.F.; Dalgarno, K.W. A review on selective laser sintering/melting (SLS/SLM) of aluminium alloy powders: Processing, microstructure, and properties. Progress in Materials Science 2015, 74, 401-477. Go to original source...
    17. Pal, S.; Lojen, G.; Hudak, R.; Rajtukova, V.; Brajlih, T.; Kokol, V.; Drstvenšek, I. As-fabricated surface morphologies of Ti-6Al-4V samples fabricated by different laser processing parameters in selective laser melting. Additive Manufacturing 2020, 33, 101147. Go to original source...
    18. Pal, S.; Tiyyagura, H.R.; Drstvenšek, I.; Kumar, C.S. The Effect of Post-processing and Machining Process Parameters on Properties of Stainless Steel PH1 Product Produced by Direct Metal Laser Sintering. Procedia Engineering 2016, 149, 359-365. Go to original source...
    19. Yan, C.; Hao, L.; Hussein, A.; Young, P. Ti-6Al-4V triply periodic minimal surface structures for bone implants fabricated via selective laser melting. Journal of the Mechanical Behavior of Biomedical Materials 2015, 51, 61-73. Go to original source...
    20. Qiu, C.; Panwisawas, C.; Ward, M.; Basoalto, H.C.; Brooks, J.W.; Attallah, M.M. On the role of melt flow into the surfacestructure and porosity development during selective laser melting. Acta Materialia 2015, 96, 72-79. Go to original source...
    21. Ramadani, R.; Pal, S.; Kegl, M.; Predan, J.; Drstvenšek, I.; Pehan, S.; Belšak, A. Topology optimization and additive manufacturing in producing lightweight and low vibration gear body. The International Journal of Advanced Manufacturing Technology 2021, 113, 3389-3399. Go to original source...
    22. Ramadani, R.; Belsak, A.; Kegl, M.; Predan, J.; Pehan, S. Topology Optimization Based Design of Lightweight and Low Vibration Gear Bodies. International Journal of Simulation Modelling 2018, 17, 92-104. Go to original source...
    23. Ramadani, R.; Kegl, M.; Predan, J.; Belšak, A.; Pehan, S. Influence of cellular lattice body structure on gear vibration induced by meshing. Strojniski Vestnik/Journal of Mechanical Engineering 2018, 64, 611-620.
    24. Pal, S.; Lojen, G.; Kokol, V.; Drstvenšek, I. Reducing porosity at the starting layers above supporting bars of the parts made by Selective Laser Melting. Powder Technology 2019, 355, 268-277. Go to original source...
    25. Gåård, A.; Krakhmalev, P.; Bergström, J. Microstructural characterization and wear behavior of (Fe,Ni)-TiC MMC prepared by DMLS. Journal of Alloys and Compounds 2006, 421, 166-171. Go to original source...
    26. Rabemanantsoa, M.; Pierre, S. An artificial intelligence approach for generating assembly sequences in CAD/CAM. Artificial Intelligence in Engineering 1996, 10, 97-107. Go to original source...
    27. Subashini, M.M.; Sahoo, S.K.; Sunil, V.; Easwaran, S. A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques. Expert Systems with Applications 2016, 43, 186-196. Go to original source...
    28. Pal, S.; Gubeljak, N.; Hudák, R.; Lojen, G.; Rajťúková, V.; Brajlih, T.; Drstvenšek, I. Evolution of the metallurgical properties of Ti-6Al-4V, produced with different laser processing parameters, at constant energy density in selective laser melting. Results in Physics 2020, 17, 103186. Go to original source...
    29. Enneti, R.K.; Morgan, R.; Atre, S. V Effect of process parameters on the Selective Laser Melting (SLM) of tungsten. International Journal of Refractory Metals and Hard Materials 2018, 71, 315-319. Go to original source...
    30. Pal, S.; Brajlih, T.; Drstvensek, I. Physical Behaviors of Materials in Selective Laser Melting Process. In DAAAM International Scientific Book 2018; 2018; pp. 239-256. Go to original source...
    31. Xu, W.; Sun, S.; Elambasseril, J.; Liu, Q.; Brandt, M.; Qian, M. Ti-6Al-4V Additively Manufactured by Selective Laser Melting with Superior Mechanical Properties. JOM 2015, 67, 668-673. Go to original source...
    32. Fernandez-Vicente, M.; Calle, W.; Ferrandiz, S.; Conejero, A. Effect of Infill Parameters on Tensile Mechanical Behavior in Desktop 3D Printing. 3D Printing and AdditiveManufacturing 2016, 3, 183-192. Go to original source...
    33. Ong, Y.S.; Keane, A.J. A domain knowledge based search advisor for design problem solving environments. Engineering Applications of Artificial Intelligence 2002, 15, 105-116. Go to original source...
    34. Takaichi, A.; Suyalatu; Nakamoto, T.; Joko, N.; Nomura, N.; Tsutsumi, Y.; Migita, S.; Doi, H.; Kurosu, S.; Chiba, A.; et al. Microstructures and mechanical properties of Co-29Cr-6Mo alloy fabricated by selective laser melting process for dental applications. Journal of the Mechanical Behavior of Biomedical Materials 2013, 21, 67-76. Go to original source...
    35. Sun, J.; Yang, Y.; Wang, D. Mechanical properties of a Ti6Al4V porous structure produced by selective laser melting. Materials and Design 2013, 49, 545-552. Go to original source...
    36. Khokhar, S.; Mohd Zin, A.A.B.; Mokhtar, A.S.B.; Pesaran, M. A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renewable and Sustainable Energy Reviews 2015, 51, 1650-1663. Go to original source...
    37. Leu, M.C.; Elmaraghy, H.A.; Nee, A.Y.C.; Ong, S.K.; Lanzetta, M.; Putz, M.; Zhu, W.; Bernard, A. CAD model based virtual assembly simulation, planning and training. CIRP Annals - Manufacturing Technology 2013, 62, 799-822. Go to original source...
    38. Joskowicz, L. Mechanism comparison and classification for design. Research in Engineering Design 1990, 1, 149-166. Go to original source...
    39. Chandra, B.; Paul Varghese, P. Moving towards efficient decision tree construction. Information Sciences 2009, 179, 1059-1069. Go to original source...

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