Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

References

• Bhadeshia, H.K.D.H. Nickel Based Superalloys. http://www.msm.cam.ac.uk/phase-trans/2003/nickel.html (accessed July 15, 2013).
   Google Scholar
• Pollock, T.M.; Tin, S. Nickel-based superalloys for advanced turbine engines: chemistry, microstructure and properties. Journal of Propulsion and Power 2006, 22, 361–374.
   Web of Science ®Google Scholar
• Callister, W.D.; Rethwisch, D.G. Materials Science and Engineering: An Introduction, 8th edition; John Wiley & Sons: New York, 2010.
   Google Scholar
• Zhang, Q.; Mahfouf, M. A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels. Engineering Applications of Artificial Intelligence 2010, 23, 660–675.
   Web of Science ®Google Scholar
• Datta, S.; Zhang, Q.; Sultana, N.; Mahfouf, M. Optimal design of titanium alloys for prosthetic applications using a multiobjective evolutionary algorithm. Materials and Manufacturing Processes 2013, 28 (7), 741–745.
   Web of Science ®Google Scholar
• Xu, W.; Rivera-Diaz-del-Castillo, P.E.J.; van der Zwaag, S. Designing nanoprecipitation strengthened UHS stainless steels combining genetic algorithms and thermodynamics. Computational Materials Science 2008, 44 (2), 678–689.
   Web of Science ®Google Scholar
• Yegorov-Egorov, I.N.; Dulikravich, G.S. Chemical composition design of superalloys for maximum stress, temperature and time-to-rupture using self-adapting response surface optimization. Materials and Manufacturing Processes 2005, 20 (3), 569–590.
   Web of Science ®Google Scholar
• Ikeda, Y. A new method of alloy design using a genetic algorithm and molecular-dynamics simulation and its application to nickel-based superalloys. Materials Transactions, Japan Institute of Metals 1997, 38 (9), 771–779.
   Web of Science ®Google Scholar
• Tancret, F. Computational thermodynamics and genetic algorithms to design affordable γ′-strengthened nickel–iron based superalloys. Modelling and Simulation in Materials Science and Engineering 2012, 20 (4), 1–6.
   Web of Science ®Google Scholar
• Kumar, A.; Chakrabarti, D.; Chakraborti, N. Data-driven Pareto optimization for microalloyed steels using genetic algorithms. Steel Research International 2012, 83, 169–174.
   Web of Science ®Google Scholar
• Horstemeyer, M.F. Integrated Computational Materials Engineering (ICME) for Metals Using Multiscale Modeling to Invigorate Engineering Design with Science, TMS (The Minerals, Metals & Materials Society); John Willey & Sons: Hoboken, NJ, 2012.
   Google Scholar
• Yegorov-Egorov, I.N.; Dulikravich, G.S. Design of alloy's concentrations for optimized strength, temperature, time-to- rupture, cost and weight. In Sixth International Special Emphasis Symposium on Superalloys 718, 625, 706 and Derivatives, Pittsburgh, PA, October 2–5; Loria, E.A. Ed.; TMS (The Minerals, Metals & Materials Society): Pittsburgh, PA, 2005; 419–428.
   Google Scholar
• Sobol, I.M. On the distribution of points in a cube and the approximate evaluation of integrals. USSR Computational Mathematics and Mathematical Physics 1967, 7 (4), 86–112.
   Google Scholar
• http://en.wikipedia.org/wiki/Pearson_productmoment_correlation_coefficient (accessed July 15, 2013).
   Google Scholar
• Pettersson, F.; Chakraborti, N.; Saxén, H. A genetic algorithms based multi-objective neural net applied to noisy blast furnace data. Applied Soft Computing 2007, 7 (1), 387–397.
   Web of Science ®Google Scholar
• Mondal, D.N.; Sarangi, K.; Pettersson, F.; Sen, P.K.; Saxén, H.; Chakraborti, N. Cu—Zn separation by supported liquid membrane analyzed through multi-objective genetic algorithms. Hydrometallurgy 2011, 107, 112–123.
   Web of Science ®Google Scholar
• Giri, B.K.; Pettersson, F.; Saxén, H.; Chakraborti, N. Genetic programming evolved through bi-objective genetic algorithms applied to a blast furnace. Materials and Manufacturing Processes 2013, 28 (7), 776–782.
   Web of Science ®Google Scholar
• Giri, B.K.; Hakanen, J.; Miettinen, K.; Chakraborti, N. Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives. Applied Soft Computing 2013, 13 (5), 2613–2623.
   Web of Science ®Google Scholar
• Saxén, H.; Pettersson, F. Method for the selection of inputs and structure of feedforward neural networks. Computers & Chemical Engineering 2006, 30 (6), 1038–1045.
   Web of Science ®Google Scholar
• Miettinen, K. Nonlinear Multiobjective Optimization; Kluwer: Boston, MA, 1999.
   Google Scholar
• Chakraborti, N. Promise of multiobjective genetic algorithms in coating performance formulation. Surface Engineering 2014, 30 (2), 79–82.
   Web of Science ®Google Scholar
• Lu, C.W.; Xi, X.B.; Li, M.J.; Liu, L.; Yue, B.H.; Zhang, L.M. Using support vector machine for materials design. Advances in Manufacturing 2013, V1 (2), 151–159.
   Web of Science ®Google Scholar
• Fan, H.Y.; Dulikravich, G.S.; Han, Z.H. Aerodynamic data modeling using support vector machines. Inverse Problems in Science and Engineering 2005, 13 (3), 261–278.
   Web of Science ®Google Scholar
• Cawley, G.C.; Talbot, N.L.C. On over-fitting in model selection and subsequent selection bias in performance evaluation. The Journal of Machine Learning Research 2010, 11, 2079–2107.
   Web of Science ®Google Scholar
• Data Mining, research.cs.queensu.ca/home/xiao/dm.html (accessed August 10, 2014).
   Google Scholar
• Lampinen, J.; Vehtari, A. Bayesian approach for neural networks—review and case studies. Neural Networks 2001, 14 (3), 257–274.
   PubMed Web of Science ®Google Scholar
• Bhadeshia, H.K.D.H. Neural networks in materials science. ISIJ International 1999, 39 (10), 966–979.
   Web of Science ®Google Scholar
• Sumptor, B.G.; Noid, D.W. On the design, analysis, and characterization of materials using computational neural networks. Annual Review of Materials Science 1996, 26, 223–277.
   Google Scholar
• Thermocalc, www.thermocalc.com/solutions/by-application/alloy-development/ (accessed July 25, 2014).
   Google Scholar
• Bhattacharya, B.; Kumar, G.R.D.; Agarwal, A.; Erkoç, S.; Singh, A.; Chakraborti, N. Analyzing Fe–Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms. Computational Materials Science 2009, 46 (4), 821–827.
   Web of Science ®Google Scholar
• Rajak, P.; Tewary, U.; Das, S.; Bhattacharya, B.; Chakraborti, N. Phases in Zn-coated Fe analyzed through an evolutionary meta-model and multi-objective Genetic Algorithms. Computational Materials Science 2011, 50 (8), 2502–2516.
   Web of Science ®Google Scholar
• Rajak, P.; Ghosh, S.; Bhattacharya, B.; Chakraborti, N. Pareto-optimal analysis of Zn-coated Fe in the presence of dislocations using genetic algorithms. Computational Materials Science 2012, 62, 266–271.
   Web of Science ®Google Scholar
• Bansal, A.; Barman, A.; Ghosh, S.; Chakraborti, N. Designing Cu-Zr glass using multiobjective genetic algorithm and evolutionary neural network metamodels–based classical molecular dynamics simulation. Materials and Manufacturing Processes 2013, 28 (7), 733–740.
   Web of Science ®Google Scholar
• Collet, P. Genetic programming. In Handbook of Research on Nature Inspired Computing for Economics and Management; Rennard, J.-P. Ed.; Idea: Hershey, PA, 2007; 59–73.
   Google Scholar
• http://en.wikipedia.org/wiki/Akaike_information_criterion (accessed July 15, 2013).
   Google Scholar
• Coello Coello, C.A.; Van Veldhuizen, D.A.; Lamont, G.B. Evolutionary Algorithms for Solving Multi-Objective Problems; Kluwer: New York, NY, 2002.
   Google Scholar
• Poli, R.; Langdon, W.B.; McPhee, N.F. A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (with contributions by J.R. Koza), 2008 (accessed July 25, 2013).
   Google Scholar
• Kovacic, M.; Sarler, B. Genetic programming and soft-annealing productivity. Materials and Technology 2011, 45 (5), 427–432.
   Web of Science ®Google Scholar
• Gusel, L.; Brezocnik, M. Application of genetic programming for modelling of material characteristics. Expert Systems With Applications – ESWA 2011, 38 (12), 15014–15019.
   Web of Science ®Google Scholar
• Brezocnik, M.; Kovacic, M.; Ficko, M. Prediction of surface roughness with genetic programming. Journal of Materials Processing Technology 2004, 157, 28–36.
   Web of Science ®Google Scholar
• Jha, R.; Sen, P.K.; Chakraborti, N. Multi‐objective genetic algorithms and genetic programming models for minimizing input carbon rates in a blast furnace compared with a conventional analytic approach. Steel Research International 2014, 85 (2), 219–232.
   Web of Science ®Google Scholar
• Deb, K. Optimization in Engineering Design Algorithms and Examples, 2nd edition; PHI Learning: New Delhi, India, 2012.
   Google Scholar
• Pettersson, F.; Chakraborti, N.; Singh, S.B. Neural networks analysis of steel plate processing augmented by multi-objective genetic algorithms. Steel Research International 2007, 78 (12), 890.
   Web of Science ®Google Scholar
• Datta, S.; Chattopadhyay, P.P. Soft computing techniques in advancement of structural metals. International Materials Reviews 2013, 58 (8), 475–504.
   Web of Science ®Google Scholar
• Yegorov-Egorov, I.N.; Dulikravich, G.S.; Colaco, M.J. Optimizing chemistry of bulk metallic glasses for improved thermal stability. Modelling and Simulation in Materials Science and Engineering 2008, 16, 075010.
   Web of Science ®Google Scholar
• Bhargava, S.; Dulikravich, G.S.; Murty, S.; Agarwal, A.; Colaco, M.J. Stress corrosion cracking resistant aluminum alloys: optimizing concentrations of alloying elements and tempering. Materials and Manufacturing Processes 2011, 26 (3), 363–374.
   Web of Science ®Google Scholar
• Colaço, M.J.; Sahoo, D.; Dulikravich, G.S. A response surface method-based hybrid optimizer. Inverse Problems in Science and Engineering 2008, 16 (6), 717–741.
   Web of Science ®Google Scholar
• Dulikravich, G.S.; Egorov, I.N. Inverse design of alloys' chemistry for specified thermo-mechanical properties by using multi-objective optimization. Chapter 8 In Computational Methods for Applied Inverse Problems; Wang, Y.F. Yagola, A.G. Yang, C.C. Eds.; De Gruyter and Higher Education Press: China, 2012.
   Google Scholar
• http://www.factsage.com/ (accessed July 20, 2013).
   Google Scholar
• Johnson, N. Strategic Minerals of the United States. http://academic.emporia.edu/abersusa/go336/natalie/newindex.htm
   Google Scholar
• Paszkowicz, W. Genetic algorithms, a nature-inspired tool: a survey of applications in materials science and related fields: part II. Materials and Manufacturing Processes 2013, 28 (7), 708–725.
   Web of Science ®Google Scholar
• Tancret, F. Computational thermodynamics, Gaussian processes and genetic algorithms: combined tools to design new alloys. Modelling and Simulation in Materials Science and Engineering 2013, 21, 1–9.
   Web of Science ®Google Scholar
• Sun, F.; Zhang, J.X.; Tian, Y. Calculation of alloying effect of Ruthenium in Ni-based single-crystal superalloys. Computational Materials Science 2012, 60, 163–167.
   Web of Science ®Google Scholar