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Volumn 24, Issue 1, 2009, Pages 10-15

Strength of ferritic steels: Neural networks and genetic programming

Author keywords

Creep strength; Ferritic steels; Genetic programming; Hot strength; Neural networks; Steel

Indexed keywords

COMPUTER PROGRAMMING; CREEP; FERRITE; FERRITIC STEEL; GENETIC ALGORITHMS; GENETIC PROGRAMMING; STEEL; STEEL METALLOGRAPHY; VEGETATION;

EID: 57649231437     PISSN: 10426914     EISSN: 15322475     Source Type: Journal    
DOI: 10.1080/10426910802539796     Document Type: Conference Paper
Times cited : (33)

References (32)
  • 1
    • 35148828026 scopus 로고    scopus 로고
    • Hot-strength of creep-resistant ferritic steels and relationship to creep-rupture data
    • Dimitriu, R.C.; Bhadeshia, H.K.D.H. Hot-strength of creep-resistant ferritic steels and relationship to creep-rupture data. Materials Science and Technology 2007, 23, 1127-1131.
    • (2007) Materials Science and Technology , vol.23 , pp. 1127-1131
    • Dimitriu, R.C.1    Bhadeshia, H.K.D.H.2
  • 3
    • 34249851467 scopus 로고    scopus 로고
    • Prediction of the bending capability of rolled metal sheet by genetic programming
    • Kovacic, M.; Uratnik, P.; Berezocnik, M. Prediction of the bending capability of rolled metal sheet by genetic programming. Materials and Manufacturing Processes 2007, 22, 634-640.
    • (2007) Materials and Manufacturing Processes , vol.22 , pp. 634-640
    • Kovacic, M.1    Uratnik, P.2    Berezocnik, M.3
  • 4
    • 19944387343 scopus 로고    scopus 로고
    • Modelling of hot yield stresses curves for carbon silicon steel by genetic programming
    • Kovacic, M.; Berezocnick, M.; Turk, R. Modelling of hot yield stresses curves for carbon silicon steel by genetic programming. Materials and Manufacturing Processes 2005, 20, 543-551.
    • (2005) Materials and Manufacturing Processes , vol.20 , pp. 543-551
    • Kovacic, M.1    Berezocnick, M.2    Turk, R.3
  • 5
    • 0034888269 scopus 로고    scopus 로고
    • Design of ferritic creep-resistant steels
    • Bhadeshia, H.K.D.H. Design of ferritic creep-resistant steels. ISIJ International 2001, 41, 621-640.
    • (2001) ISIJ International , vol.41 , pp. 621-640
    • Bhadeshia, H.K.D.H.1
  • 6
    • 0035247432 scopus 로고    scopus 로고
    • A comparison of linear genetic programming and neural network in medical data mining
    • Brameier, M.; Banzhaf, W. A comparison of linear genetic programming and neural network in medical data mining. IEE Transactions on Evolutionary Computation 2001, 5 (1), 17-26.
    • (2001) IEE Transactions on Evolutionary Computation , vol.5 , Issue.1 , pp. 17-26
    • Brameier, M.1    Banzhaf, W.2
  • 7
    • 0002704818 scopus 로고
    • Practical Bayesian framework of back-propagation networks
    • MacKay, D.J.C. Practical Bayesian framework of back-propagation networks. Neural Computation 1992, 4, 448-472.
    • (1992) Neural Computation , vol.4 , pp. 448-472
    • MacKay, D.J.C.1
  • 8
    • 0001025418 scopus 로고
    • Bayesian interpolation
    • MacKay, D.J.C. Bayesian interpolation. Neural Computation 1992, 4, 415-447.
    • (1992) Neural Computation , vol.4 , pp. 415-447
    • MacKay, D.J.C.1
  • 9
    • 0033309515 scopus 로고    scopus 로고
    • Neural networks in materials science
    • Bhadeshia, H.K.D.H. Neural networks in materials science. ISIJ International 1999, 39, 966-979.
    • (1999) ISIJ International , vol.39 , pp. 966-979
    • Bhadeshia, H.K.D.H.1
  • 12
    • 0030369186 scopus 로고    scopus 로고
    • Prediction of low cycle fatigue lives of low alloy steels
    • Goswami, T. Prediction of low cycle fatigue lives of low alloy steels. ISIJ International 1996, 36, 354-360.
    • (1996) ISIJ International , vol.36 , pp. 354-360
    • Goswami, T.1
  • 14
    • 0002434657 scopus 로고    scopus 로고
    • Improved ferrite number prediction in stainless steel arc welds using artificial neural networks
    • Vitek, J.M.; Iskander, Y.S.; Oblow, E.M. Improved ferrite number prediction in stainless steel arc welds using artificial neural networks. Welding Journal, Research Supplement 2000, 79, 33s-50s.
    • (2000) Welding Journal, Research Supplement , vol.79
    • Vitek, J.M.1    Iskander, Y.S.2    Oblow, E.M.3
  • 15
    • 0035023573 scopus 로고    scopus 로고
    • Prediction of mechanical properties in steel heat treatment process using neural networks
    • Tenner, J.; Linken, D.A.; Morris, P.F.; Bailey, T.J. Prediction of mechanical properties in steel heat treatment process using neural networks. Ironmaking and Steelmaking 2001, 28, 15-22.
    • (2001) Ironmaking and Steelmaking , vol.28 , pp. 15-22
    • Tenner, J.1    Linken, D.A.2    Morris, P.F.3    Bailey, T.J.4
  • 16
    • 9144234370 scopus 로고    scopus 로고
    • Artificial neural networks for modelling of the impact toughness of steel
    • Dunne, D.; Tsuei, H.; Sterjovski, Z. Artificial neural networks for modelling of the impact toughness of steel. ISIJ International 2004, 44, 1599-1607.
    • (2004) ISIJ International , vol.44 , pp. 1599-1607
    • Dunne, D.1    Tsuei, H.2    Sterjovski, Z.3
  • 17
    • 0345359456 scopus 로고    scopus 로고
    • Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network
    • Guo, Z.; Sha, W. Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network. Computational Materials Science 2004, 29, 12-28.
    • (2004) Computational Materials Science , vol.29 , pp. 12-28
    • Guo, Z.1    Sha, W.2
  • 18
    • 33748173246 scopus 로고    scopus 로고
    • Neural network analysis of strain induced transformation behaviour of retained austenite in TRIP-aided steels
    • Mukherjee, M.; Singh, S.B.; Mohanty, O.N. Neural network analysis of strain induced transformation behaviour of retained austenite in TRIP-aided steels. Materials Science and Engineering A 2006, 434A, 237-245.
    • (2006) Materials Science and Engineering A , vol.434 A , pp. 237-245
    • Mukherjee, M.1    Singh, S.B.2    Mohanty, O.N.3
  • 19
    • 10844253085 scopus 로고    scopus 로고
    • Optimizing parameters of supervised learning techniques (ANN) for precise mapping of the input-output relationship in TMCP steels
    • Datta, S.; Banerjee, M.K. Optimizing parameters of supervised learning techniques (ANN) for precise mapping of the input-output relationship in TMCP steels. Scandinavian Journal of Metallurgy 2004, 33, 310-315.
    • (2004) Scandinavian Journal of Metallurgy , vol.33 , pp. 310-315
    • Datta, S.1    Banerjee, M.K.2
  • 22
    • 0036808510 scopus 로고    scopus 로고
    • Genetic programming and evolutionary generalisation
    • Kushchu, I. Genetic programming and evolutionary generalisation. IEEE Transactions on Evolutionary Computation 2002, 6(5), 431-442.
    • (2002) IEEE Transactions on Evolutionary Computation , vol.6 , Issue.5 , pp. 431-442
    • Kushchu, I.1
  • 23
    • 4444250325 scopus 로고    scopus 로고
    • Genetic algorithms in materials design and processing
    • Chakraborti, N. Genetic algorithms in materials design and processing. International Materials Reviews 2004, 49, 246-260.
    • (2004) International Materials Reviews , vol.49 , pp. 246-260
    • Chakraborti, N.1
  • 24
    • 0035415174 scopus 로고    scopus 로고
    • Grammatical evolution in evolutionary computation
    • O'Neill, M.; Ryan, C. Grammatical evolution in evolutionary computation. IEEE Transactions 2001, 5 (4), 349-358.
    • (2001) IEEE Transactions , vol.5 , Issue.4 , pp. 349-358
    • O'Neill, M.1    Ryan, C.2
  • 26
    • 57649162508 scopus 로고
    • Exact bias and mean square error of k-class estimators
    • Sawa T. Exact bias and mean square error of k-class estimators. Econometrica 1970, 38 (4), 138-146.
    • (1970) Econometrica , vol.38 , Issue.4 , pp. 138-146
    • Sawa, T.1
  • 27
    • 0015347237 scopus 로고
    • Root mean square error in encoded digital telemetry
    • Kilgus, C.C.; Gore, W.C. Root mean square error in encoded digital telemetry. IEE Transactions on Communications 1972, 18 (5), 315-320.
    • (1972) IEE Transactions on Communications , vol.18 , Issue.5 , pp. 315-320
    • Kilgus, C.C.1    Gore, W.C.2
  • 29
    • 0033732270 scopus 로고    scopus 로고
    • A comparison of predictive measures of problem difficulty in evolutionary algorithms. Evolutionary Computation
    • Naudts, B.; Kallel, L. A comparison of predictive measures of problem difficulty in evolutionary algorithms. Evolutionary Computation, IEE Transactions 2000, 4 (1), 1-15.
    • (2000) IEE Transactions , vol.4 , Issue.1 , pp. 1-15
    • Naudts, B.1    Kallel, L.2
  • 30
    • 0031185996 scopus 로고    scopus 로고
    • Structural optimization by genetic algorithms with tournament selection
    • Yang, J.P.; Soh, C.K. Structural optimization by genetic algorithms with tournament selection. Journal of Computing in Civil Engineering 1997, 11 (3), 897-903.
    • (1997) Journal of Computing in Civil Engineering , vol.11 , Issue.3 , pp. 897-903
    • Yang, J.P.1    Soh, C.K.2
  • 31
    • 0028496754 scopus 로고
    • An edge-detection technique using genetic algorithm-based optimization
    • Bhandarkar, S.M.; Zhang, Y.Q.; Potter, W.D. An edge-detection technique using genetic algorithm-based optimization. Pattern Recognition 1994, 27(9), 1159-1180.
    • (1994) Pattern Recognition , vol.27 , Issue.9 , pp. 1159-1180
    • Bhandarkar, S.M.1    Zhang, Y.Q.2    Potter, W.D.3
  • 32
    • 57649182950 scopus 로고
    • NRIM data sheets 1b, 3b, 8b, 11b, 12b, 17b, 18b, 19b, 20b, 21b
    • Technical Report; National Research Institute for Metals: Tokyo, Japan
    • Kojchi, Y.; Hiroshi I.; Hideo, T.; Masayoshi, Y.; Osamu, K.; Kiyoshi, K.; Kazuhio, K. NRIM data sheets 1b, 3b, 8b, 11b, 12b, 17b, 18b, 19b, 20b, 21b. Technical Report; National Research Institute for Metals: Tokyo, Japan, 1994.
    • (1994)
    • Kojchi, Y.1    Hiroshi, I.2    Hideo, T.3    Masayoshi, Y.4    Osamu, K.5    Kiyoshi, K.6    Kazuhio, K.7


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.