PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Evolution of δ phase precipitates in Inconel 625 superalloy additively manufactured by laser powder bed fusion and its modeling with fuzzy logic

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Experimental and modeling studies of the evolution of plate-like δ phase precipitates in Inconel 625 superalloy additively manufactured by the laser powder bed fusion process are performed. The maximum Feret diameter and the number of particles per unit area are used as parameters describing the size and distribution of the δ phase precipitates. On the basis of microstructural analysis and quantitative image analysis, the effect of time and temperature on the development of δ phase precipitates is determined. The distinct differences in the intensity of precipitation, growth, and coarsening of the δ phase precipitates during annealing at temperatures of 700 and 800 °C up to 2000 h are shown. The experimental results are compared with computational data obtained by thermodynamic modeling. Using the experimentally determined parameters of the δ phase precipitates in different variants of annealing, a fuzzy logic-based phase distribution model is designed. Since the quantity of available data was too small to train a model with the machine learning approach, expert knowledge is used to design the rules, while numerical data are used for its validation. Designed rules, as well as reasoning methodology are described. The proposed model is validated by comparing it with the experimental results. It can be used to predict the size and number density of the δ phase precipitates in the additively manufactured Inconel 625, subjected to long-term annealing at temperatures of 700-800 °C. Due to limited experimental data, the quality of assurance is not perfect, but warrants preliminary research.
Rocznik
Strony
art. no. e86, 2023
Opis fizyczny
Bibliogr. 44 poz., rys., tab., wykr.
Twórcy
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30‑059 Kraków, Poland
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30‑059 Kraków, Poland
autor
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30‑059 Kraków, Poland
autor
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30‑059 Kraków, Poland
autor
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30‑059 Kraków, Poland
Bibliografia
  • 1. DebRoy T, Wei HL, Zuback JS, Mukherjee T, Elmer JW, Milewski JO, Beese AM, Wilson-Heid A, De A, Zhang W. Additive manufacturing of metallic components-process, structure and properties. Prog Mater Sci. 2018;92:112-224. https://doi.org/10.1016/j.pmatsci.2017.10.001.
  • 2. Yap CY, Chua CK, Dong ZL, Liu ZH, Zhang DQ, Loh LE, Sing SL. Review of selective laser melting: materials and applications. Appl Phys Rev. 2015;2:1-22. https://doi.org/10.1063/1.493592 6.
  • 3. Shoemaker LE. Alloys 625 and 725: trends in properties and applications. In: Proceedings of international symposium superalloys various derivatives. 2005. pp. 409-18. https://doi.org/10.7449/2005/superalloys_2005_409_418.
  • 4. Marchese G, Garmendia Colera X, Calignano F, Lorusso M, Biamino S, Minetola P, Manfredi D. Characterization and comparison of Inconel 625 processed by selective laser melting and laser metal deposition. Adv Eng Mater. 2017;19:1-9. https://doi.org/10.1002/adem.201600635.
  • 5. Staroń S, Dubiel B, Gola K, Kalemba-Rec I, Gajewska M, Pasiowiec H, Wrobel R, Leinenbach C. Quantitative microstructural characterization of precipitates and oxide inclusions in Inconel 625 superalloy additively manufactured by L-PBF method. Metall Mater Trans A Phys Metall Mater Sci. 2022;53:2459-79. https://doi.org/10.1007/s11661-022-06679-1.
  • 6. Gola K, Dubiel B, Kalemba-Rec I. Microstructural changes in Inconel 625 alloy fabricated by laser-based powder bed fusion process and subjected to high-temperature annealing. J MaterEng Perform. 2020;29:1528-34. https://doi.org/10.1007/s11665-020-04605-3.
  • 7. Lass EA, Stoudt MR, Williams ME, Katz MB, Levine LE, Phan TQ, Gnaeupel-Herold TH, Ng DS. Formation of the Ni3Nb δ-phase in stress-relieved Inconel 625 produced via laser powder-bed fusion additive manufacturing. Metall Mater Trans A Phys Metall Mater Sci. 2017;48:5547-58. https://doi.org/10.1007/s11661-017-4304-6.
  • 8. Nguejio J, Szmytka F, Hallais S, Tanguy A, Nardone S, Godino Martinez M. Comparison of microstructure features and mechanical properties for additive manufactured and wrought nickel alloys 625. Mater Sci Eng A. 2019;764:1-37. https://doi.org/10.1016/j.msea.2019.138214.
  • 9. Liu X, Fan J, Zhang P, Xie J, Chen F, Liu D, Yuan R, Tang B, Kou H, Li J. Temperature dependence of deformation behavior, microstructure evolution and fracture mechanism of Inconel 625 superalloy. J Alloys Compd. 2021;869:1-12. https://doi.org/10.1016/j.jallcom.2021.159342.
  • 10. Suave LM, Bertheau D, Cormier J, Villechaise P, Soula A, Hervier Z, Hamon F, Laigo J. Impact of thermomechanical aging on alloy 625 high temperature mechanical properties. In: 8th international symposium superalloy 718 derivative. 2014. pp. 317-31. https://doi.org/10.1002/9781119016854.ch26.
  • 11. Stoudt MR, Lass EA, Ng DS, Williams ME, Zhang F, Campbell CE, Lindwall G, Levine LE. The influence of annealing temperature and time on the formation of δ-phase in additively-manufactured Inconel 625. Metall Mater Trans A Phys Metall Mater Sci. 2018;49:3028-37. https://doi.org/10.1007/s11661-018-4643-y.
  • 12. Andersson JO, Helander T, Hoglund L, Shi P, Sundman B. Thermo-Calc & DICTRA, computational tools for materials science, Calphad Comput. Coupl Phase Diagr Thermochem. 2002;26:273-312. https://doi.org/10.1016/S0364-5916(02)00037-8.
  • 13. Cortial F, Corrieu JM, Vernot-Loier C. Influence of heat treatments on microstructure, mechanical properties, and corrosion resistance of weld alloy 625. Metall Mater Trans A. 1995;26A:1273-86. https://doi.org/10.1088/2053-1591/abb858.
  • 14. Mittra J, Banerjee S, Tewari R, Dey GK. Fracture behavior of Alloy 625 with different precipitate microstructures. Mater Sci Eng A. 2013;574:86-93. https://doi.org/10.1016/j.msea.2013.03.021.
  • 15. Suave LM, Bertheau D, Cormier J, Villechaise P, Soula A, Hervier Z, Laigo J. Impact of microstructural evolutions during thermal aging of Alloy 625 on its monotonic mechanical properties. MATEC Web Conf. 2014;14:1-6. https://doi.org/10.1051/matecconf/20141421001.
  • 16. Zhang Z, Yavas D, Liu Q, Wu D. Effect of build orientation and raster pattern on the fracture behavior of carbon fiber reinforced polymer composites fabricated by additive manufacturing. Addit Manuf. 2021;47:1-9. https://doi.org/10.1016/j.addma.2021.102204.
  • 17. Yadroitsev I, Thivillon L, Bertrand P, Smurov I. Strategy of manufacturing components with designed internal structure by selective laser melting of metallic powder. Appl Surf Sci. 2007;254:980-3. https://doi.org/10.1016/j.apsusc.2007.08.046.
  • 18. Yan X, Gao S, Chang C, Huang J, Khanlari K, Dong D, Ma W, Fenineche N, Liao H, Liu M. Effect of building directions on the surface roughness, microstructure, and tribological properties of selective laser melted Inconel 625. J Mater Process Technol. 2021;288:1-11. https://doi.org/10.1016/j.jmatprotec.2020.116878.
  • 19. Sanchez S, Gaspard G, Hyde CJ, Ashcroft IA, Ravi GA, Clare AT. The creep behaviour of nickel alloy 718 manufactured by laser powder bed fusion. Mater Des. 2021;204:1-17. https://doi.org/10.1016/j.matdes.2021.109647.
  • 20. Sundararaman M, Mukhopadhyay P, Banerjee S. Precipitation of the δ-Ni3Nb phase in two nickel base superalloys. Metall Trans A. 1988;19:453-65. https://doi.org/10.1007/BF02649259.
  • 21. Lindwall G, Campbell CE, Lass EA, Zhang F, Stoudt MR, Allen AJ, Levine LE. Simulation of TTT curves for additively manufactured Inconel 625. Metall Mater Trans A Phys Metall Mater Sci. 2019;50:457-67. https://doi.org/10.1007/s11661-018-4959-7.
  • 22. Khosravani MR, Rezaei S, Faroughi S, Reinicke T. Experimental and numerical investigations of the fracture in 3D-printed openhole plates. Theor Appl Fract Mech. 2022;121:1-10. https://doi.org/10.1016/j.tafmec.2022.103543.
  • 23. Lu LX, Sridhar N, Zhang YW. Phase field simulation of powder bed-based additive manufacturing. Acta Mater. 2018;144:801-9. https://doi.org/10.1016/J.ACTAMAT.2017.11.033.
  • 24. Liu PW, Ji YZ, Wang Z, Qiu CL, Antonysamy AA, Chen LQ, Cui XY, Chen L. Investigation on evolution mechanisms of site-specific grain structures during metal additive manufacturing. JMater Process Technol. 2018;257:191-202. https://doi.org/10.1016/J.JMATPROTEC.2018.02.042.
  • 25. Liu P, Wang Z, Xiao Y, Horstemeyer MF, Cui X, Chen L. Insight into the mechanisms of columnar to equiaxed grain transition during metallic additive manufacturing. Addit Manuf. 2019;26:22-9. https://doi.org/10.1016/J.ADDMA.2018.12.019.
  • 26. Fleck M, Schleifer F, Holzinger M, Glatzel U. Phase-field modeling of precipitation growth and ripening during industrial heat treatments in Ni-base superalloys. Metall Mater Trans A Phys Metall Mater Sci. 2018;49:4146-57. https://doi.org/10.1007/S11661-018-4746-5.
  • 27. Holzinger M, Schleifer F, Glatzel U, Fleck M. Phase-field modeling of γ′-precipitate shapes in nickel-base superalloys and their classification by moment invariants. Eur Phys J B. 2019;92:1-9. https://doi.org/10.1140/epjb/e2019-100256-1.
  • 28. Chen M, Du Q, Shi R, Fu H, Liu Z, Xie J. Phase field simulation of microstructure evolution and process optimization during homogenization of additively manufactured Inconel 718 alloy. Front Mater. 2022;9:1-14. https://doi.org/10.3389/fmats.2022.1043249.
  • 29. Aghaeipoor F, Javidi MM. MOKBL+MOMs: an interpretable multi-objective evolutionary fuzzy system for learning high-dimensional regression data. Inf Sci (NY). 2019;496:1-24. https://doi.org/10.1016/j.ins.2019.04.035.
  • 30. Sousa MJ, Moutinho A, Almeida M. Classification of potential fire outbreaks: a fuzzy modeling approach based on thermal images. Expert Syst Appl. 2019;129:216-32. https://doi.org/10.1016/j.eswa.2019.03.030.
  • 31. Hullermeier E. From knowledge-based to data-driven fuzzy modeling. Informatik-Spektrum. 2015;38:500-9. https://doi.org/10.1007/s00287-015-0931-8.
  • 32. Nakayashiki T, Kaneko T (2018) Learning of evaluation functions via self-play enhanced by checkmate search. In: Proceedings of 2018 conference technology applied artificial intelligence. TAAI 2018, pp 126-31. https://doi.org/10.1109/TAAI.2018.00036.
  • 33. Macioł P, Szeliga D, Sztangret Ł. Methodology for metamodelling of microstructure evolution: precipitation kinetic case study. Int J Mater Form. 2018;11:867-78. https://doi.org/10.1007/s12289-017-1396-x.
  • 34. Macioł A, Macioł P. The use of fuzzy rule-based systems in the design process of the metallic products on example of microstructure evolution prediction. J Intell Manuf. 2022;33:1991-2012. https://doi.org/10.1007/s10845-022-01949-6.
  • 35. Macioł A, Rębiasz B. Multicriteria decision analysis (MCDA) methods in life-cycle assessment (LCA): a comparison of private passenger vehicles. Oper Res Decis. 2018;28:5-26. https://doi.org/10.5277/ord180101.
  • 36. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671-5. https://doi.org/10.1038/nmeth.2089.
  • 37. Li M, Wilkinson D, Patchigolla K. Comparison of particle size distributions measured using different techniques. Part Sci Technol. 2005;23:265-84. https://doi.org/10.1080/02726350590955912.
  • 38. Buades A, Coll B, Morel J-M. Non-local means denoising. Image Process Line. 2011;1:208-12. https://doi.org/10.5201/ipol.2011.bcm_nlm.
  • 39. Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud. 1975;7:1-13. https://doi.org/10.1016/S0020-7373(75)80002-2.
  • 40. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern. 1985;SMC-15:116-32. https://doi.org/10.1109/TSMC.1985.6313399.
  • 41. Macioł A, Macioł P. Design of Takagi-Sugeno fuzzy systems by learning from examples in case a number of available data is not sufficient. Nauk Tech Technol Tom. 2021;2:93-120. https://doi.org/10.7494/978-83-66727-48-9_7.
  • 42. Macioł A, Macioł P, Mrzygłod B. Prediction of forging dies wear with the modified Takagi-Sugeno fuzzy identification method. Mater Manuf Process. 2020;35:700-13. https://doi.org/10.1080/10426914.2020.1747627.
  • 43. Suave LM, Cormier J, Villechaise P, Soula A, Hervier Z, Bertheau D, Laigo J. Microstructural evolutions during thermal aging of alloy 625: impact of temperature and forming process. Metall Mater Trans A Phys Metall Mater Sci. 2014;45:2963-82. https://doi.org/10.1007/s11661-014-2256-7.
  • 44. Deschamps A, Hutchinson CR. Precipitation kinetics in metallic alloys: experiments and modeling. Acta Mater. 2021;220:1-23. https://doi.org/10.1016/j.actamat.2021.117338.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a9c6a6c7-88ab-4e71-a024-af85341c9c6f
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.