Data-driven evaluation of fatigue performance of additive manufactured parts using miniature specimens
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摘要
This overview firstly introduces the state-of-the-art research progress in length scale-related fatigue performance of conventionally-fabricated metals evaluated by miniature specimens. Some key factors for size effects sensitive to microstructures including the specimen thickness, grain size and a ratio between them are highlighted to summarize some general rules for size effects. Then, ongoing research progress and new challenges in evaluating the fatigue performance of additive manufactured parts controlled by location-specific defects, microstructure heterogeneities as well as mechanical anisotropy using miniature specimen testing technique are discussed and addressed. Finally, a potential roadmap to establish a data-driven evaluation platform based on a large number of miniature specimen-based experiment data,theoretical computations and the 'big data' analysis with machine learning is proposed. It is expected that this overview would provide a novel strategy for the realistic evaluation and fast qualification of fatigue properties of additive manufactured parts we have been facing to.
This overview firstly introduces the state-of-the-art research progress in length scale-related fatigue performance of conventionally-fabricated metals evaluated by miniature specimens. Some key factors for size effects sensitive to microstructures including the specimen thickness, grain size and a ratio between them are highlighted to summarize some general rules for size effects. Then, ongoing research progress and new challenges in evaluating the fatigue performance of additive manufactured parts controlled by location-specific defects, microstructure heterogeneities as well as mechanical anisotropy using miniature specimen testing technique are discussed and addressed. Finally, a potential roadmap to establish a data-driven evaluation platform based on a large number of miniature specimen-based experiment data,theoretical computations and the 'big data' analysis with machine learning is proposed. It is expected that this overview would provide a novel strategy for the realistic evaluation and fast qualification of fatigue properties of additive manufactured parts we have been facing to.
This overview firstly introduces the state-of-the-art research progress in length scale-related fatigue performance of conventionally-fabricated metals evaluated by miniature specimens. Some key factors for size effects sensitive to microstructures including the specimen thickness, grain size and a ratio between them are highlighted to summarize some general rules for size effects. Then, ongoing research progress and new challenges in evaluating the fatigue performance of additive manufactured parts controlled by location-specific defects, microstructure heterogeneities as well as mechanical anisotropy using miniature specimen testing technique are discussed and addressed. Finally, a potential roadmap to establish a data-driven evaluation platform based on a large number of miniature specimen-based experiment data,theoretical computations and the 'big data' analysis with machine learning is proposed. It is expected that this overview would provide a novel strategy for the realistic evaluation and fast qualification of fatigue properties of additive manufactured parts we have been facing to.
引文
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[30] A.W. Thompson, W.A. Backofen, Acta Metall. 19(1971)597–606.
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[32] Q.S. Pan, Q.H. Lu, L. Lu, Acta Mater. 61(2013)1383–1393.
[33] B. Zhang, Z.M. Song, L.M. Lei, L. Kang, G.P. Zhang, J. Mater. Sci. Technol. 30(2014)1284–1288.
[34] Y.F. Ma, Z.M. Song, G.P. Zhang, unpublished results(2018).
[35] K. Zhang, J. Weertman, Metall. Mater. Trans. A 40(2009)2255–2263.
[36] C.Y. Dai, X.F. Zhu, G.P. Zhang, J. Mater. Sci. Technol. 25(2009)721–726.
[37] E.O. Hall, Proc. R. Soc. B 64(1951)747–753.
[38] N.J. Petch, J. Iron Steel Inst. 174(1953)25–28.
[39] A. Wimmer, A. Leitner, T. Detzel, W. Robl, W. Heinz, R. Pippan, G. Dehm, Acta Mater. 67(2014)297–307.
[40] K. Kammuri, M. Kitamura, T. Fujii, M. Kato, Mater. Trans. 56(2015)200–205.
[41] C.Y. Dai, Mechanical Behavior of Micrometer-scale Copper and Size Effects,Ph.D. Thesis, University of Chinese Academy of Sciences, 2012.
[42] J. Pegues, M. Roach, R.S. Williamson, N. Shamsaei, Int. J. Fatigue 116(2018)543–552.
[43] A.B. Spierings, T.L. Starr, K. Wegener, Rapid Prototyp. J. 19(2013)88–94.
[44] H.A. Stoffregen, K. Butterweck, E. Abele, Austin, Texas25th Solid Freeform Fabrication Symposium2014, 25th Solid Freeform Fabrication Symposium(2014)635–650.
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[46] A. Yadollahi, N. Shamsaei, Int. J. Fatigue 98(2017)14–31.
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[49] A. Yadollahi, N. Shamsaei, S.M. Thompson, A. Elwany, L. Bian, Int. J. Fatigue 94(2017)218–235.
[50] S. Beretta, S. Romano, Int. J. Fatigue 94(2017)178–191.
[51] M. Suraratchai, J. Limido, C. Mabru, R. Chieragatti, Int. J. Fatigue 30(2008)2119–2126.
[52] R. Procházka, J. Dzˇugan,IOP Conf. Ser. Mater. Sci. Eng. 179(2017), 012060.
[53] J. Martin, J. Test. Eval. 11(1983)66–74.
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[55] Y.H. Zhao, J.E. Bingert, X.Z. Liao, B.Z. Cui, K. Han, A.V. Sergueeva, A.K.Mukherjee, R.Z. Valiev, T.G. Langdon, Y.T. Zhu, Adv. Mater. 18(2006)2949–2953.
[56] V.L. Tellkamp, A. Melmed, E.J. Lavernia, Metall. Mater. Trans. 32(2001)2335–2343.
[57] K.M. Youssef, R.O. Scattergood, K.L. Murty, J.A. Horton, C.C. Koch,Appl. Phys.Lett. 87(2005), 091904.
[58] C.Y. Dai, B. Zhang, J. Xu, G.P. Zhang, Mater. Sci. Eng. A 575(2013)217–222.
[59] Y.F. Ma, Z.M. Song, S.Q. Zhang, L.J. Chen, G.P. Zhang, Acta Metall. Sin. 54(2018)1359–1367.
[60] G. Nicoletto, Procedia Struct. Integr. 8(2018)184–191.
[61] G. Nicoletto, Int. J. Fatigue 94(2017)255–262.
[62] A. Agrawal, A. Choudhary,APL Mater. 4(2016), 053208.
[63] X.L. Liao, W.F. Xu, Z.Q. Gao, in:H.S. Lee, I.S. Yoon, M.H. Aliabadi(Eds.), Key Engineering Materials, Vols. 385-387, Trans Tech Publ, Switzerland, 2008, pp.533–536, http://dx.doi.org/10.4028/www.scientific.net/KEM.385-387.533.
[64] M. Susmikanti, AIPAIP Conference Proceedings2013, AIP Conference Proceedings(2013)174–177, http://dx.doi.org/10.1063/1.4820313.
[65] G. Partheepan, D. Sehgal, R.K. Pandey, Adv. Artif. Neural. Syst. 2011(2011)1–8.
[66] M. Abendroth, M. Kuna, Eng. Fract. Mech. 73(2006)710–725.
[67] G. Partheepan, D.K. Sehgal, R.K. Pandey, Comput. Mater. Sci. 44(2008)523–530.
[68] W. Zhang, Z. Bao, S. Jiang, J. He, Materials 9(2016)483.
[69] B. Meredig, Curr. Opin. Solid State Mater. Sci. 21(2017)159–166.
[70] K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh, Nature 559(2018)547–555.
[71] X. Yu, Machine Learning Application in the Life Time of Materials, 2017, July16 https://arxiv.org/abs/1707.04826.
[2] J.J. Lewandowski, M. Seifi, Annu. Rev. Mater. Res. 46(2016)151–186.
[3] S.R. Daniewicz, N. Shamsaei, Int. J. Fatigue 94(2017), 167-167.
[4] M. Seifi, M. Gorelik, J. Waller, N. Hrabe, N. Shamsaei, S. Daniewicz, J.J.Lewandowski, JOM 69(2017)439–455.
[5] M. Seifi, A. Salem, D. Satko, J. Shaffer, J.J. Lewandowski, Int. J. Fatigue 94(2017)263–287.
[6] A. Yadollahi, N. Shamsaei, S.M. Thompson, D.W. Seely, Mater. Sci. Eng. A 644(2015)171–183.
[7] D.U. Furrer, D.M. Dimiduk, J.D. Cotton, C.H. Ward, Integr. Mater. Manuf. Innov.6(2017)249–263.
[8] J. Dzugan, R. Prochazka, M. Rund, P. Podany, P. Konopik, M. Seifi, J.J.Lewandowski, Mater. Charact. 143(2018)94–109.
[9] S. Romano, A. Brand?o, J. Gumpinger, M. Gschweitl, S. Beretta, Mater. Des. 131(2017)32–48.
[10] J. Dzˇugan, R. Procházka, P. Konopík, in:M.A. Sokolov, E. Lucon(Eds.), Small Specimen Test Techniques, 6th Volume, ASTM International, New York, 2015,pp. 12–29, http://dx.doi.org/10.1520/STP157620140022.
[11] R. Procházka, J. Dzˇugan, M. K?vér, Arch. Mater. Sci. Eng. 76(2015)134–139.
[12] M.N. Gussev, R.H. Howard, K.A. Terrani, K.G. Field, Nucl. Eng. Des. 320(2017)298–308.
[13] J. Dzˇugan, P. Konopík, R. Procházka, Z. Trojanová, Mater. Sci. Forum 879(2017)471–476.
[14] K. Kumar, A. Pooleery, K. Madhusoodanan, R.N. Singh, J.K. Chakravartty, B.K.Dutta, R.K. Sinha, Procedia Eng. 86(2014)899–909.
[15] J. Dzugan, M. Sibr, P. Konopík, R. Procházka, M. Rund,IOP Conf. Ser. Mater. Sci.Eng. 179(2017), 012019.
[16] A.V. Sergueeva, J. Zhou, B.E. Meacham, D.J. Branagan, Mater. Sci. Eng. A 526(2009)79–83.
[17] M.G.D. Geers, W.A.M. Brekelmans, P.J.M. Janssen, Int. J. Solids Struct. 43(2006)7304–7321.
[18] J. Dzugan, R. Prochazka, P. Konopik, ASME, in:R. Crane, K. Hojo, S.X. Xu, R.C.Cipolla(Eds.), Pressure Vessels and Piping Conference Volume 1A:Codes and Standards(2017)1–6, http://dx.doi.org/10.1115/PVP2017-66174.
[19] J. Dzugan, P. Konopik, M. Rund, R. Prochazka, ASME, in:S.X. Xu, K. Hojo, R.C.Cipolla(Eds.), Pressure Vessels and Piping Conference Volume 1A:Codes and Standards(2015)1–8, http://dx.doi.org/10.1115/PVP2015-45958.
[20] E. Arzt, Acta Mater. 46(1998)5611–5626.
[21] G.P. Zhang, C.A. Volkert, R. Schwaiger, P. Wellner, E. Arzt, O. Kraft, Acta Mater.54(2006)3127–3139.
[22] Q. Yu, Z.W. Shan, J. Li, X.X. Huang, L. Xiao, J. Sun, E. Ma, Nature 463(2010)335–338.
[23] J.R. Greer, W.C. Oliver, W.D. Nix, Acta Mater. 53(2005)1821–1830.
[24] M.D. Uchic, D.M. Dimiduk, J.N. Florando, W.D. Nix, Science 305(2004)986–989.
[25] C. Keller, E. Hug, X. Feaugas, Int. J. Plast. 27(2011)635–654.
[26] P. Lukas, L. Kunz, Mater. Sci. Eng. 85(1987)67–75.
[27] C.Y. Dai, G.P. Zhang, C. Yan, Philos. Mag. 91(2011)932–945.
[28] G. Khatibi, A. Betzwar-Kotas, V. Groger, B. Weiss, Fatigue Fract. Eng. Mater.Struct. 28(2005)723–733.
[29] J. Zhang, Y. Jiang, Int. J. Plasticity 22(2006)536–556.
[30] A.W. Thompson, W.A. Backofen, Acta Metall. 19(1971)597–606.
[31] B. Weiss, V. Groger, G. Khatibi, A. Kotas, P. Zimprich, R. Stickler, B. Zagar, Sens.Actuators A Phys. 99(2002)172–182.
[32] Q.S. Pan, Q.H. Lu, L. Lu, Acta Mater. 61(2013)1383–1393.
[33] B. Zhang, Z.M. Song, L.M. Lei, L. Kang, G.P. Zhang, J. Mater. Sci. Technol. 30(2014)1284–1288.
[34] Y.F. Ma, Z.M. Song, G.P. Zhang, unpublished results(2018).
[35] K. Zhang, J. Weertman, Metall. Mater. Trans. A 40(2009)2255–2263.
[36] C.Y. Dai, X.F. Zhu, G.P. Zhang, J. Mater. Sci. Technol. 25(2009)721–726.
[37] E.O. Hall, Proc. R. Soc. B 64(1951)747–753.
[38] N.J. Petch, J. Iron Steel Inst. 174(1953)25–28.
[39] A. Wimmer, A. Leitner, T. Detzel, W. Robl, W. Heinz, R. Pippan, G. Dehm, Acta Mater. 67(2014)297–307.
[40] K. Kammuri, M. Kitamura, T. Fujii, M. Kato, Mater. Trans. 56(2015)200–205.
[41] C.Y. Dai, Mechanical Behavior of Micrometer-scale Copper and Size Effects,Ph.D. Thesis, University of Chinese Academy of Sciences, 2012.
[42] J. Pegues, M. Roach, R.S. Williamson, N. Shamsaei, Int. J. Fatigue 116(2018)543–552.
[43] A.B. Spierings, T.L. Starr, K. Wegener, Rapid Prototyp. J. 19(2013)88–94.
[44] H.A. Stoffregen, K. Butterweck, E. Abele, Austin, Texas25th Solid Freeform Fabrication Symposium2014, 25th Solid Freeform Fabrication Symposium(2014)635–650.
[45] I.H. Oh, N. Nomura, N. Masahashi, S. Hanada, Scr. Mater. 49(2003)1197–1202.
[46] A. Yadollahi, N. Shamsaei, Int. J. Fatigue 98(2017)14–31.
[47] D. Herzog, V. Seyda, E. Wycisk, C. Emmelmann, Acta Mater. 117(2016)371–392.
[48] B. Torries, A. Imandoust, S. Beretta, S. Shao, N. Shamsaei, JOM 70(2018)1853–1862.
[49] A. Yadollahi, N. Shamsaei, S.M. Thompson, A. Elwany, L. Bian, Int. J. Fatigue 94(2017)218–235.
[50] S. Beretta, S. Romano, Int. J. Fatigue 94(2017)178–191.
[51] M. Suraratchai, J. Limido, C. Mabru, R. Chieragatti, Int. J. Fatigue 30(2008)2119–2126.
[52] R. Procházka, J. Dzˇugan,IOP Conf. Ser. Mater. Sci. Eng. 179(2017), 012060.
[53] J. Martin, J. Test. Eval. 11(1983)66–74.
[54] Y.H. Zhao, Y.Z. Guo, Q. Wei, A.M. Dangelewicz, C. Xu, Y.T. Zhu, T.G. Langdon,Y.Z. Zhou, E.J. Lavernia, Scr. Mater. 59(2008)627–630.
[55] Y.H. Zhao, J.E. Bingert, X.Z. Liao, B.Z. Cui, K. Han, A.V. Sergueeva, A.K.Mukherjee, R.Z. Valiev, T.G. Langdon, Y.T. Zhu, Adv. Mater. 18(2006)2949–2953.
[56] V.L. Tellkamp, A. Melmed, E.J. Lavernia, Metall. Mater. Trans. 32(2001)2335–2343.
[57] K.M. Youssef, R.O. Scattergood, K.L. Murty, J.A. Horton, C.C. Koch,Appl. Phys.Lett. 87(2005), 091904.
[58] C.Y. Dai, B. Zhang, J. Xu, G.P. Zhang, Mater. Sci. Eng. A 575(2013)217–222.
[59] Y.F. Ma, Z.M. Song, S.Q. Zhang, L.J. Chen, G.P. Zhang, Acta Metall. Sin. 54(2018)1359–1367.
[60] G. Nicoletto, Procedia Struct. Integr. 8(2018)184–191.
[61] G. Nicoletto, Int. J. Fatigue 94(2017)255–262.
[62] A. Agrawal, A. Choudhary,APL Mater. 4(2016), 053208.
[63] X.L. Liao, W.F. Xu, Z.Q. Gao, in:H.S. Lee, I.S. Yoon, M.H. Aliabadi(Eds.), Key Engineering Materials, Vols. 385-387, Trans Tech Publ, Switzerland, 2008, pp.533–536, http://dx.doi.org/10.4028/www.scientific.net/KEM.385-387.533.
[64] M. Susmikanti, AIPAIP Conference Proceedings2013, AIP Conference Proceedings(2013)174–177, http://dx.doi.org/10.1063/1.4820313.
[65] G. Partheepan, D. Sehgal, R.K. Pandey, Adv. Artif. Neural. Syst. 2011(2011)1–8.
[66] M. Abendroth, M. Kuna, Eng. Fract. Mech. 73(2006)710–725.
[67] G. Partheepan, D.K. Sehgal, R.K. Pandey, Comput. Mater. Sci. 44(2008)523–530.
[68] W. Zhang, Z. Bao, S. Jiang, J. He, Materials 9(2016)483.
[69] B. Meredig, Curr. Opin. Solid State Mater. Sci. 21(2017)159–166.
[70] K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh, Nature 559(2018)547–555.
[71] X. Yu, Machine Learning Application in the Life Time of Materials, 2017, July16 https://arxiv.org/abs/1707.04826.