Predicting hardness profile of steel specimens subjected to Jominy test using an artificial neural network and electromagnetic nondestructive techniques

References

• Handbook A. Heat treating. Vol. 4. Materials Park, OH: ASM International; 1991. p. 860.
   Google Scholar
• Thelning K-E. Steel and its heat treatment. Butterworth-Heinemann; 2013.
   Google Scholar
• Kandpal BC, Chutani A, Gulia A, et al. A review on Jominy test and determination of effect of alloying elements on hardenability of steel using Jominy end quench test. Int J Adv Eng Technol. 2011;1(3):65.
   Google Scholar
• Yekinni A, Agunsoye J, Bello S, et al. Fabrication of end quenched machine: hardenability evaluation. J Miner Mater Charact Eng. 2014;2(2):107–113.
   Google Scholar
• Hassan MA, Mehdi M, Owais M, et al. Design and fabrication of Jominy end-quench testing machine: hardenability evaluation of AISI-8620 case-hardening steel. J Test Eval. 2017;46(1):305–316.
   Web of Science ®Google Scholar
• Yazdi AZ, Sajjadi SA, Zebarjad SM, et al. Prediction of hardness at different points of Jominy specimen using quench factor analysis method. J Mater Process Technol. 2008;199(1–3):124–129.
   Web of Science ®Google Scholar
• Vermeulen W, Van der Wolk P, De Weijer A, et al. Prediction of Jominy hardness profiles of steels using artificial neural networks. J Mater Eng Perform. 1996;5(1):57–63.
   Web of Science ®Google Scholar
• Song Y-P, Liu G-Q, Liu S-X, et al. Improved nonlinear equation method for numerical prediction of Jominy end-quench curves. J Iron and Steel Res Int. 2007;14(1):37–41.
   Web of Science ®Google Scholar
• Altpeter I, Tschuncky R, Szielasko K. Electromagnetic techniques for materials characterization. Materials characterization using Nondestructive Evaluation (NDE) Methods. Elsevier; 2016. p. 225–262.
   Google Scholar
• Hao X, Yin W, Strangwood M, et al. Off-line measurement of decarburization of steels using a multifrequency electromagnetic sensor. Scr Mater. 2008;58(11):1033–1036.
   Web of Science ®Google Scholar
• Kahrobaee S, Kashefi M. Hardness profile plotting using multi-frequency multi-output electromagnetic sensor. NDT E Int. 2011;44(4):335–338.
   Web of Science ®Google Scholar
• Kashefi M, Kahrobaee S. Dual-frequency approach to assess surface hardened layer using NDE technology. J Mater Eng Perform. 2013;22(4):1108–1112.
   Web of Science ®Google Scholar
• Lo CCH, Kinser ER, Melikhov Y, Jiles DC. Magnetic nondestructive characterization of case depth in surface‐hardened steel components. In: Thompson DO, Chimenti DE, editors. Vol. 820. Review of progress in quantitative nondestructive evaluation 25B. AIP Conference Proceedings; 2006. p. 1253–1260.
   Google Scholar
• Kobayashi S, Takahashi H, Kamada Y. Evaluation of case depth in induction-hardened steels: magnetic hysteresis measurements and hardness-depth profiling by differential permeability analysis. J Magn Magn Mater. 2013;343:112–118.
   Web of Science ®Google Scholar
• Stupakov A, Farda R, Neslušan M, et al. Evaluation of a nitrided case depth by the magnetic Barkhausen noise. J Nondestr Eval. 2017;36(4):73.
   Web of Science ®Google Scholar
• Amiri MS, Kashefi M. Application of eddy current nondestructive method for determination of surface carbon content in carburized steels. NDT E Int. 2009;42(7):618–621.
   Web of Science ®Google Scholar
• Kashefi M, Kahrobaee S. Determination of presence of undesirable carbides at surface of cast iron parts using differential eddy current technique. Mater Sci Technol. 2013;29(8):990–994.
   Web of Science ®Google Scholar
• Alonso U, Ortega N, Sanchez JA, et al. In-process prediction of the hardened layer in cylindrical traverse grind-hardening. Int J Adv Manuf Technol. 2014;71(1–4):101–108.
   Web of Science ®Google Scholar
• Kahrobaee S, Zohourvahid Karimi E. Characterisation of work-hardening in Hadfield steel using non-destructive eddy current method. Case Stud NondestrTest Eval. 2019;34(2):178–192.
   Google Scholar
• Franco FA, González M, De Campos M, et al. Relation between magnetic Barkhausen noise and hardness for Jominy quench tests in SAE 4140 and 6150 steels. J Nondestr Eval. 2013;32(1):93–103.
   Web of Science ®Google Scholar
• Karpathy A. Cs231n: Convolutional neural networks for visual recognition [Internet]. Stanford (CA): Stanford University; [cited 2019 May 18]. Available from: http://cs231n.stanford.edu/2016/.
   Google Scholar
• Hassoun MH. Fundamentals of artificial neural networks. Cambridge (MA): MIT press; 1995.
   Google Scholar
• Aggarwal CC. Neural networks and deep learning. Yorktown Heights (NY): Springer; 2018.
   Google Scholar
• Kahrobaee S, Norouzi Sahraei H, Ahadi Akhlaghi I. Nondestructive characterization of microstructure and mechanical properties of heat treated H13 tool steel using magnetic hysteresis loop methodology. Res Nondestr Eval. 2019;30(5):303–315.
   Web of Science ®Google Scholar
• Xiucheng L, Ruihuan Z, Bin W, et al. Quantitative prediction of surface hardness in 12CrMoV steel plate based on magnetic Barkhausen noise and tangential magnetic field measurements. J Nondestr Eval. 2018;37(2):38.
   Web of Science ®Google Scholar
• Ahadi Akhlaghi I, Kahrobaee S, Karami Nezhad K. An accurate non-destructive method for determining mechanical properties of plain carbon steel parts using MHL and GRNN. Case Stud NondestrTest Eval. 2020;1–19. DOI:10.1080/10589759.2020.1740703.
   Google Scholar
• Ghanei S, Vafaeenezhad H, Kashefi M, et al. Design of an expert system based on neuro-fuzzy inference analyzer for on-line microstructural characterization using magnetic NDT method. J Magn Magn Mater. 2015;379:131–136.
   Web of Science ®Google Scholar
• Mirzaee A, Kahrobaee S, Ahadi Akhlaghi I. Non-destructive determination of microstructural/mechanical properties and thickness variations in API X65 steel using magnetic hysteresis loop and artificial neural networks. Case Stud NondestrTest Eval. 2019;35(2):190–206.
   Google Scholar
• Sahebalam A, Kashefi M, Kahrobaee S. Comparative study of eddy current and Barkhausen noise methods in microstructural assessment of heat treated steel parts. Case Stud NondestrTest Eval. 2014;29(3):208–218.
   Google Scholar
• Kahrobaee S, Kashefi M. Electromagnetic nondestructive evaluation of tempering process in AISI D2 tool steel. J Magn Magn Mater. 2015;382:359–365.
   Web of Science ®Google Scholar
• Kahrobaee S, Haghighi MS, Akhlaghi IA. Improving nondestructive characterization of dual phase steels using data fusion. J Magn Magn Mater. 2018;458:317–326.
   Web of Science ®Google Scholar
• Kahrobaee S, Hejazi T-H, Akhlaghi IA. Electromagnetic methods to improve the nondestructive characterization of induction hardened steels: A statistical modeling approach. Surf Coat Technol. 2019;380:125074.
   Web of Science ®Google Scholar
• Totten GE. Steel heat treatment: metallurgy and technologies. Bosa Roca: CRC Press Inc; 2006.
   Google Scholar
• Abbaszadeh K, Kheirandish S, Saghafian H. The effect of lower bainite volume fraction on tensile and impact properties of D6AC medium carbon low alloy ultrahigh strength steel. Iranian J Mater Sci Eng. 2010;7(3):31–38.
   Google Scholar
• Rumiche F, Indacochea J, Wang M. Assessment of the effect of microstructure on the magnetic behavior of structural carbon steels using an electromagnetic sensor. J Mater Eng Perform. 2008;17(4):586–593.
   Web of Science ®Google Scholar
• Johnson M, Lo C, Hentscher S, Kinser E. Analysis of conductivity and permeability profiles in hardened steel. In: Udpa L, Bowler N, editorsElectromagnetic nondestructive evaluation (IX), IOS, Amsterdam; 2005. p. 135–142.
   Google Scholar
• Saquet O, Chicois J, Vincent A. Barkhausen noise from plain carbon steels: analysis of the influence of microstructure. Mater Sci Eng A. 1999;269(1–2):73–82.
   Web of Science ®Google Scholar
• Karimian N, Wilson J, Peyton A, et al. Differential permeability behaviour of P9 and T22 power station steels. J Magn Magn Mater. 2014;352:81–90.
   Web of Science ®Google Scholar
• Kahrobaee S, Kashefi M. Microstructural characterization of quenched AISI D2 tool steel using magnetic/electromagnetic nondestructive techniques. IEEE Trans Magn. 2015;51(9):1–7.
   Web of Science ®Google Scholar
• Specht DF. A general regression neural network. IEEE Trans Neural Networks. 1991;2(6):568–576.
   PubMed Web of Science ®Google Scholar