Domains of steels with identical properties

Materials and Manufacturing Processes

Volumn 24, Issue 1, 2009, Pages 53-58

  Joo, Minsung ^a   Ryu, Joohyun ^a   Bhadeshia, H K D H ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

Ambiguity; Genetic algorithms; Mechanical properties; Neural network; Steel

Indexed keywords

CHEMICAL PROPERTIES; CHROMIUM; COPPER; DATABASE SYSTEMS; DIESEL ENGINES; GENETIC ALGORITHMS; MANGANESE; MANGANESE COMPOUNDS; MECHANICAL PROPERTIES; METALLIC COMPOUNDS; MOLYBDENUM; NIOBIUM; SEMANTICS; STEEL;

AMBIGUITY; BAYESIAN FRAMEWORKS; CHEMICAL COMPOSITIONS; COILING TEMPERATURES; DEFORMATION TEMPERATURES; DO-MAINS; INPUT PARAMETERS; LARGE DATABASES; NEURAL NETWORK MODELS; ROLLED STEELS; STEEL PRODUCTIONS; TENSILE ELONGATIONS; THERMOMECHANICAL PROCESSING; ULTIMATE TENSILE STRENGTHS;

NEURAL NETWORKS;

EID: 57649192459     PISSN: 10426914     EISSN: 15322475     Source Type: Journal    
DOI: 10.1080/10426910802543657     Document Type: Conference Paper

References (29)

1. Denton, B.; Gupta, D.; Jawahir, K. Managing increasing product variety at integrated steel mills. Interfaces 2003, 33, 41-53.
2. MacKay, D.J.C. Bayesian interpolation. Neural Computation 1992, 4, 415-447.
3. MacKay, D.J.C. A practical Bayesian framework for backpropagation networks. Neural Computation 1992, 4, 448-472.
4. MacKay, D.J.C. Evidence framework applied to classification networks. Neural Computation 1992, 4, 720-736.
5. MacKay, D.J.C. Information Theory, Inference and Learning Algorithms; Cambridge University Press: Cambridge, U.K., 2003.
6. Bhadeshia, H.K.D.H. Neural networks in materials science. ISIJ International 1999, 39, 966-979.
7. Bishop, C.M. Neural Networks for Pattern Recognition; Oxford University Press: Oxford, 1995.
8. Singh, S.B.; Bhadeshia, H.K.D.H.; MacKay, D.J.C. Neural network analysis of steel plate processing. Ironmaking and Steelmaking 1998, 25, 355-365.
9. Kermanpur, A.; Ebnonnasir, A.; Hedayati, M. A novel analytical-artificial neural network model to improve efficiency of high pressure descaling nozzles in hot strip rolling of steels. Materials Science and Technology 2007, 23, 951-957.
10. Son, J.-S.; Lee, D.-M.; Kim, I.-S.; Choi, S.-G. A study on on-line learning neural network for prediction for rolling force in hot-rolling mill. Journal of Materials Processing Technology 2005, 164-165, 1612-1617.
11. Datta, S.; Banerjee, M.K. Optimizing parameters of supervised learning techniques (ANN) for precise mapping of the input-output relationship in TMCP steels. Scandanavian Journal of Metallurgy 2004, 33, 310-315.
12. Dutta, P.; Pratihar, D.K. Modeling of TIG welding process using conventional regression analysis and neural network-based approaches. Journal of Materials Processing Technology 2007, 184, 56-68.
13. Kemp, R.; Cottrell, G.A.; Bhadeshia, H.K.D.H.; Odette, G.R.; Yamamoto, T.; Kishimoto, H. Neural-network analysis of irradiation hardening in low-activation steels. Journal of Nuclear Materials 2006, 348, 311-328.
14. Capdevilla, C.; Garcia-Mateo, C.; Caballero, F.G.; Garcia de Andres, C. Neural network analysis of the influence of processing on strength and ductility of automotive low carbon sheet steels. Journal of Materials Science 2006, 38, 192-201.
15. Bhadeshia, H.K.D.H.; MacKay, D.J.C.; Svensson, L.-E. Materials Science and Technology 1995, 11, 1046-1051.
16. Singh, S.B.; Bhadeshia, H.K.D.H. Estimation of bainite plate thickness. Materials Science and Engineering A 1998, 245, 72-79.
17. Cool, T.; Bhadeshia, H.K.D.H. The yield and ultimate tensile strength of steel welds. Materials Science and Engineering 1997, 223, 186-200.
18. Chakraborti, N. Genetic algorithms in materials design and processing. International Materials Reviews 2004, 49, 246-260.
19. Delorme, A. Genetic algorithm for optimization of mechanical properties. Technical Report, University of Cambridge, June 2003.
20. Chatterjee, S.; Bhadeshia, H.K.D.H. δ-TRIP steel. Materials Science and Technology 2007, 23, 819-827.
21. Chatterjee, S. Transformations in TRIP-Assisted Steels: Microstructure and Properties. Ph.D. thesis, University of Cambridge, 2006. Available at http://www.msm.cam.ac.uk/phase-trans/2000/phd.html (last accessed Nov. 13, 2008).
22. Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning; Addison-Wesley: Reading, MA, 1989.
23. Michalewicz, Z. Genetic Algorithms+Data Structures = Evolution Programs; Springer: New York, 1996.
24. Grefenstette, J.J. Optimization of control parameters for genetic algorithms. IEEE Syst. Man. Cybern. 1986, 16, 122-128.
25. Obitko, M.; Slavik, P. Visualization of Genetic Algorithms in a Learning Environment. Spring Conference on Computer Graphics, SCCG'99, Bratislava: Comenius University, 1999; 101-106.
26. Reed, R.; Bhadeshia, H.K.D.H. Reconstructive austenite-ferrite transformation in low alloy steel. Materials Science and Technology 1992, 8, 421-435.
27. Kirkaldy, J.S.; Venugopalan, D. Prediction of microstructure and hardenability in low alloy steel. In Phase Transformations in Ferrous Alloys; Marder, A.R., Goldstein, J.I., Eds.; TMS-AIME: Warrendale, Ohio, 1984; 125-148.
28. Bhadeshia, H.K.D.H. Alternatives to the ferrite-pearlite microstructures. Materials Science Forum 1998, 29, 284-286.
29. Callister, W.D. Material Science and Engineering: An Introduction, 7th Ed.; John Wiley & Sons: New York, 2007.