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Research Papers

Prediction of martensite start temperature

Pages 1370-1375 | Received 22 Sep 2014, Accepted 30 Oct 2014, Published online: 13 Nov 2014

References

  • Payson P, and Savage CH: ‘Martensite reactions in alloy steels’, Trans. ASM, 1944, 33, 261–281.
  • Carapella LA: “‘Computing A” or MS (transformation temperature on quenching) from analysis’, Met. Prog., 1944, 46, 108–108.
  • Rowland ES and Lyle SR: ‘The application of MS points case depth measurement’, Trans. ASM, 1946, 37, 27–47.
  • Grange RA and Stewart HM: ‘The temperature range of martensite formation’, Trans. AIME, 1946, 167, 467–490.
  • Nehrenberg AE: ‘Discussion’, Trans. AIME, 1956, 167, 494–498.
  • Steven W and Haynes AG: ‘The temperature formation of martensite and bainite in low–alloy steels, some effects of chemical composition’, J. Iron Steel Inst., 1956, 183, 349–359.
  • Andrews KW: ‘Empirical formulae for calculation of some transformation temperatures’, J. Iron Steel Inst., 1965, 203, 721–727.
  • Kunitake T and Ohtani H: ‘Calculating the continuous cooling transformation characteristics of steel from its chemical composition’, Sumitomo Search, 1969, 2, 18.
  • Tamura I: ‘Steel material study on the strength’; 1970, Tokyo, Nikkan Kogyo Shinbun Ltd. In Japanese.
  • Monma K: ‘Tekko-zairyo-gaku’, chap. Martensite, 158–163; 1972, Jikkyo-Syuppan Corp, Tokyo. In Japanese.
  • Eldis GT: ‘A critical review of data sources for isothermal transformation and continuous cooling transformation diagrams’, Proc. Symp. on ‘The hardenability concepts with applications to steel’, 126–157; 1977, The Metallurgical Society of AIME, Warrendale, PA.
  • Kung CY and Rayment JJ: ‘An examination of the validity of existing empirical formulae for the calculation of MS temperature’, Metall. Trans. A, 1982, 13A, 328–331.
  • Pickering FB: ‘Physical metallurgical development of stainless steels’, in ‘Stainless steels ’84’, 2–28; 1985, London, The Institute of Metals.
  • Finkler H and Schirra M: ‘Transformation behaviour of the high temperature martensitic steels with 8%–14% chromium’, Steel Res., 1996, 67, (8), 328–342.
  • Kunitake T: ‘Prediction of Ac1, Ac3, and MS temperatures of steel by empirical formulas’, J. Jpn Soc. Heat Treat., 2001, 41, 164–169.
  • Izumiyama M, Tsuchiya M and Imai Y: ‘Effects of alloying element on supercooled A3 transformation’, J. Jpn Inst. Met., 1970, 34, (3), 291–295.
  • Bhadeshia HKDH: ‘Neural networks in materials science’, ISIJ Int., 1999, 39, 966–979.
  • Vermeulen W, Morris P, de Weijer A and van der Zwaag S: ‘Prediction of martensite start temperature using artificial neural networks’, Ironmak. Steelmak., 1996, 23, (5), 433–437.
  • Wang J, van der Wolk J and van der Zwaag S: ‘Determination of martensite start temperature in engineering steels part i. empirical relations describing the effect of steel chemistry’, Mater. Trans. JIM, 2000, 41, (7), 761–768.
  • Sourmail T and Garcia-Mateo C: ‘Critical assessment of models for predicting the MS temperature of steels’, Comput. Mater. Sci., 2005, 34, 323–334.
  • Ghosh G and Olson GB: ‘Kinetics of FCC→BCC heterogeneous martensitic nucleation, part 1: The critical driving force for athermal nucleation’, Acta Mater., 1994, 42, 3361–3370.
  • Ghosh G and Olson GB: ‘Kinetics of FCC→BCC heterogeneous martensitic nucleation, part ii: Thermal activation’, Acta Mater., 1994, 42, 3371–3379.
  • Sourmail T and Garcia-Mateo C: ‘A model for predicting the MS temperatures of steels’, Comput. Mater. Sci., 2005, 34, 213–218.
  • Sourmail T: ‘MAP_DATA_STEEL_MS_2004’, available at: http://www.msm.cam.ac.uk/map/ (accessed 11 November 2014).
  • MacKay DJC: ‘Bayesian methods for adaptive models’, PhD thesis, Caltech, Pasadena, CA, USA, 1992.
  • MacKay DJC: ‘Bayesian methods for neural networks: Theory and applications’, Cavendish Laboratory, 1995.
  • MacKay DJC: ‘Bayesian non–linear modelling with neural networks’, in ‘Mathematical modelling of weld phenomena’ (ed. Cerjak H and Bhadeshia H K D H), Vol. 3, 359–389; 1997, London, IOM.
  • Yescas M, Bhadeshia HKDH and MacKay D: ‘Estimation of the amount of retained austenite in austempered ductile irons’, Mater. Sci. Eng. A, 2001, A311, 162–173.
  • Bhadeshia HKDH: ‘Neural networks in materials science’, Stat. Anal. Data Min., 2009, 1, 296–305.
  • Capdevilla C, Caballero F and de Andrés CG: ‘Determination of MS temperature in steels: A Bayesian neural network model’, ISIJ Int., 2002, 42, 894–902.
  • Neal RM: ‘Bayesian learning for neural networks’; 1996, New York, Springer.
  • Sourmail T, Bhadeshia HKDH and MacKay D: ‘Neural network model of creep strengths of austenitic stainless steels’, Mater. Sci. Technol., 2005, 18, 655–663.
  • Yang H.-S and Bhadeshia HKDH: ‘Uncertainties in dilatometric determination of martensite start temperature’, Mater. Sci. Technol., 2007, 23, (5), 556–560.
  • Peet M: MAP_MS_MODELS_2014, Materials Algorithms Project, available at: http://www.msm.cam.ac.uk/map/ (accessed 12 December 2014).

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