Detection and evaluation of thermal aging brittleness of heat-resistant steel using magneto-acoustic compound techniques

References

• Danoix F, Auger P. Atom probe studies of the Fe-Cr system and stainless steels aged at intermediate temperature: a review. Mater Charact. 2000;44(1–2):177–201. doi:10.1016/S1044-5803(99)00048-0
   Web of Science ®Google Scholar
• Fan Y, Liu TG, Xin L, et al. Thermal aging behaviors of duplex stainless steels used in nuclear power plant: a review. J Nucl Mater. 2021;544:152693. doi:10.1016/j.jnucmat.2020.152693
   Web of Science ®Google Scholar
• Zhou YH, Liu CX, Liu YC, et al. Coarsening behavior of MX carbonitrides in type 347H heat-resistant austenitic steel during thermal aging. Int J Min Met Mater. 2016;23(3):283–293. doi:10.1007/s12613-016-1237-9
   Web of Science ®Google Scholar
• Zhou YH, Liu YC, Zhou XS, et al. Precipitation and hot deformation behavior of austenitic heat-resistant steels: a review. J Mater Sci Technol. 2017;33(12):1448–1456. doi:10.1016/j.jmst.2017.01.025
   Web of Science ®Google Scholar
• Dudko V, Fedoseeva A, Kaibyshev R. Ductile-brittle transition in a 9% Cr heat-resistant steel. Mat Sci Eng A Struct. 2017;682:73–84. doi:10.1016/j.msea.2016.11.035
   Web of Science ®Google Scholar
• Biezma MV, Martin U, Linhardt P, et al. Non-destructive techniques for the detection of sigma phase in duplex stainless steel: a comprehensive review. Eng Fail Anal. 2021;122:105227. doi:10.1016/j.engfailanal.2021.105227
   Web of Science ®Google Scholar
• Jiang SB, Tian ZL, Liu WB, et al. Microstructural evolution and hardness of a heat resistant alloy during long term aging at 700°C. J Alloy Compd. 2018;765:1267–1274. doi:10.1016/j.jallcom.2018.05.263
   Web of Science ®Google Scholar
• Brnic J, Turkalj G, Canadija M, et al. Deformation behaviour and material properties of austenitic heat-resistant steel X15CrNiSi25-20 subjected to high temperatures and creep. Mater Design. 2015;69:219–229. doi:10.1016/j.matdes.2014.12.062
   Web of Science ®Google Scholar
• Wang SS, Peng DL, Chang L, et al. Enhanced mechanical properties induced by refined heat treatment for 9Cr–0.5Mo–1.8W martensitic heat resistant steel. Mater Design. 2013;50:174–180. doi:10.1016/j.matdes.2013.01.072
   Web of Science ®Google Scholar
• Barella S, Gruttadauria A, Mapelli C, et al. Effect of heat treatment and of primary austenite grain size on the minimum size of detectable defect on 26NiCrMoV11.5 high strength steel. Adv Eng Mater. 2014;16(1):103–111. doi:10.1002/adem.201300137
   Web of Science ®Google Scholar
• Hirao M, Ogi H. Electromagnetic acoustic transducers: noncontacting ultrasonic measurements using EMATs. Springer Japan; 2017. p. 233–269.
   Google Scholar
• Shi WZ, Wu YX, Gong H, et al. Optimal design of spiral coil electromagnetic acoustic transducers considering lift-off sensitivity operating on non-ferromagnetic media. Nondestruct Test And Eva. 2018;33(1):56–74. doi:10.1080/10589759.2016.1265963
   Web of Science ®Google Scholar
• Wang P, Li YR, Yao ET, et al. Method of measuring the mechanical properties of ferromagnetic materials based on magnetostriction EMAT and sound velocity. Journal Magn Magn Mater. 2022;555:169375. doi:10.1016/j.jmmm.2022.169375
   Web of Science ®Google Scholar
• Ribichini R, Cegla F, Nagy PB, et al. Experimental and numerical evaluation of electromagnetic acoustic transducer performance on steel materials. NDT & E Internationalquery. 2012;45(1):32–38. doi:10.1016/j.ndteint.2011.08.007
   PubMed Web of Science ®Google Scholar
• Marcantonio V, Monarca D, Colantoni A, et al. Ultrasonic waves for materials evaluation in fatigue, thermal and corrosion damage: a review. Mech Syst Signal Pr. 2019;120:32–42. doi:10.1016/j.ymssp.2018.10.012
   Web of Science ®Google Scholar
• Khan SZ, Khan TM, Joya YF, et al. Assessment of material properties of AISI 316L stainless steel using non-destructive testing. Nondestruct Test And Eva. 2016;31(4):360–370. doi:10.1080/10589759.2015.1121265
   Web of Science ®Google Scholar
• Zhang SZ, Li XB, Chen C, et al. Characterization of aging treated 6061 aluminum alloy using nonlinear Rayleigh wave. J Nondestruct Eva. 2019;38(4):1–9. doi:10.1007/s10921-019-0630-5
   Web of Science ®Google Scholar
• Akhlaghi IA, Kahrobaee S, Akbarzadeh A, et al. Predicting hardness profile of steel specimens subjected to Jominy test using an artificial neural network and electromagnetic nondestructive techniques. Nondestruct Test And Eva. 2021;36(4):459–475. doi:10.1080/10589759.2020.1774585
   Web of Science ®Google Scholar
• Silva EdM, Leite JP, Leite JP, et al. Induced magnetic field used to detect the sigma phase of a 2205 duplex stainless steel. J Nondestruct Eva. 2016;35(2):1–10. doi:10.1007/s10921-016-0339-7
   Web of Science ®Google Scholar
• Silva DDS, Raimundo RA, Torquato RA, et al. Low-field magnetic analysis for sigma phase embrittlement monitoring in thermally aged 22Cr duplex stainless steel. Journal Magn Magn Mater. 2020;513:167072. doi:10.1016/j.jmmm.2020.167072
   Web of Science ®Google Scholar
• Mirzaee A, Kahrobaee S, Ahadi IA. Non-destructive determination of microstructural/mechanical properties and thickness variations in API X65 steel using magnetic hysteresis loop and artificial neural networks. Nondestruct Test And Eva. 2020;35(2):190–206. doi:10.1080/10589759.2019.1662901
   Web of Science ®Google Scholar
• Murthy GVS, Sridhar G, Kumar A, et al. Characterization of intermetallic precipitates in a Nimonic alloy by ultrasonic velocity measurements. Materials Characterizationquery. 2009;60(3):234–239. doi:10.1016/j.matchar.2008.09.009
   Web of Science ®Google Scholar
• Bouda AB, Benchaala A, Alem K. Ultrasonic characterization of materials hardness. Ultrasonics. 2000;38(1–8):224–227. doi:10.1016/S0041-624X(99)00081-5
   PubMed Web of Science ®Google Scholar
• Huang S, and Wang S. New technologies in electromagnetic non-destructive testing. Singapore: Springer; 2016 .p 185–202.
   Google Scholar
• Arenas MP, Silveira RM, Pacheco CJ, et al. Magnetic evaluation of the external surface in cast heat-resistant steel tubes with different aging states. Journal Magn Magn Mater. 2018;456:346–352. doi:10.1016/j.jmmm.2018.02.051
   Web of Science ®Google Scholar
• Xie SJ, Tian MM, Xiao P, et al. A hybrid nondestructive testing method of pulsed eddy current testing and electromagnetic acoustic transducer techniques for simultaneous surface and volumetric defects inspection. NDT E Int. 2017;86:153–163. doi:10.1016/j.ndteint.2016.12.006
   Web of Science ®Google Scholar
• Guo W, Gao B, Tian GY, et al. Physic perspective fusion of electromagnetic acoustic transducer and pulsed eddy current testing in non-destructive testing system. Philos T R Soc A. 2020;378(2182):20190608. doi:10.1098/rsta.2019.0608
   Google Scholar
• Liu ZH, Zhao X, Pei N, et al. Numerical decoupling study of EMAT testing signal for ferromagnetic materials. IEEE Sens J. 2019;20(7):3476–3486. doi:10.1109/JSEN.2019.2959601
   Web of Science ®Google Scholar
• Song HD, Xiao Q, Wang G, et al. A composite approach of electromagnetic acoustic transducer and eddy current for inner and outer corrosion defects detection. IEEE T Instrum Meas. 2023;72:1–11. doi:10.1109/TIM.2023.3238689
   PubMed Web of Science ®Google Scholar
• Zhao X, Liu ZH, Gong Y, et al. Performance degradation detection of 12CrMoV steel by magneto-acoustic compound inspection method. NDT E Int. 2021;124:102525. doi:10.1016/j.ndteint.2021.102525
   Web of Science ®Google Scholar
• Zhao X, Liu ZH, Guo YH, et al. An advanced magnetoacoustic compound inspection method of high-order mode EMAT. IEEE Sens J. 2021;22(1):229–239. doi:10.1109/JSEN.2021.3130121
   Web of Science ®Google Scholar
• Chopra OK. Estimation of fracture toughness of cast stainless steels during thermal aging in LWR systems-Revision 1. No. ANL-93/22. Argonne, IL (United States): Argonne National Lab. (ANL); 1994. doi:10.2172/985103.
   Google Scholar
• Youn GG, Nam HS, Kim YJ, et al. Numerical prediction of thermal aging and cyclic loading effects on fracture toughness of cast stainless steel CF8A: experimental and numerical study. International Journal Of Mechanical Sciencesquery. 2019;163:105120. doi:10.1016/j.ijmecsci.2019.105120.
   Web of Science ®Google Scholar
• Zuo C, Qian JM, Feng SJ, et al. Deep learning in optical metrology: a review. Light-SCI Appl. 2022;11(1):1–39. doi:10.1038/s41377-022-00714-x
   PubMed Web of Science ®Google Scholar
• Zhu HC, Tu J, Cai C, et al. A fast signal-processing method for electromagnetic ultrasonic thickness measurement of pipelines based on UKF and SMO. Energies. 2022;15(18):6554. doi:10.3390/en15186554
   Web of Science ®Google Scholar
• Sun HY, Peng LS, Lin JM, et al. Microcrack defect quantification using a focusing high-order SH guided wave EMAT: the physics-informed deep neural network GuwNet. IEEE T Ind Inform. 2021;18(5):3235–3247. doi:10.1109/TII.2021.3105537
   Web of Science ®Google Scholar
• Veloni A, Miridakis N, Boukouvala E. Digital and statistical signal processing. USA: CRC Press; 2019.
   Google Scholar
• Schumacker RE. Learning statistics using R. USA: SAGE; 2015; p. 330–337.
   Google Scholar