Optimization of annealing cycle parameters of dual phase and interstitial free steels by multiobjective genetic algorithms

References

• Bouaziz, O.; Zurob, H.; Huang, M. Driving force and logic of development of advanced high strength steels for automotive applications. Steel Research International 2013, 84 (10), 937–947.
   Web of Science ®Google Scholar
• Bleck, W.; Phiu-On, K. Microalloying of cold-formable multi phase steel grades; Conference Proceedings on ‘Microalloying for New Steel Processes and Applications, San Sebastian, Spain, 2005; published in Materials Science Forum, Trans Tech Publications, 2005, 500–501, 97–112.
   Google Scholar
• Senuma, T. Physical metallurgy of modern high strength steel sheets. ISIJ International 2001, 41 (6), 520–532.
   Web of Science ®Google Scholar
• Matlock, D.K.; Speer, J.G.; Moor, E.D.; Gibbs, P.J. Recent developments in advanced high strength sheet steels for automotive applications: An overview. JESTECH 2012, 15 (1), 1–12.
   Google Scholar
• Song, R.; Ponge, D.; Raabe, D.; Speer, J.G.; Matlock, D.K. Overview of processing, microstructure and mechanical properties of ultrafine grained bcc steels. Materials Science and Engineering: A 2006, 441 (1–2), 1–17.
   Web of Science ®Google Scholar
• Cízek, J.; Janecek, M.; Krajnak, T.; Straska, J.; Hruska, P.; Gubicza, J.; Kim, H.S. Structural characterization of ultrafine-grained interstitial-free steel prepared by severe plastic deformation. Acta Materialia 2016, 105, 258–272.
   Web of Science ®Google Scholar
• Tutum, C.C.; Deb, K.; Baran, I. Constrained efficient global optimization for pultrusion process. Materials and Manufacturing Processes 2015, 30, 538–551.
   Web of Science ®Google Scholar
• Bertelli, F.; Silva-Santos, C.H.; Bezerra, D.J.; Cheung, N.; Garcia, A. An effective inverse heat transfer procedure based on evolutionary algorithms to determine cooling conditions of a steel continuous casting machine. Materials and Manufacturing Processes 2015, 30, 414–424.
   Web of Science ®Google Scholar
• Tyagi, R.; Pant, M.; Negi, Y.S.; Ali, M. Optimization of the electrical performance of polymeric films. Materials and Manufacturing Processes 2015, 30, 464–473.
   Web of Science ®Google Scholar
• Kappes, B.B.; Ciobanu, C.V. Materials screening through GPU accelerated topological mapping. Materials and Manufacturing Processes 2015, 30, 529–537.
   Web of Science ®Google Scholar
• Ngoc-Trung, N.; Hariharan, K.; Chakraborti, N.; Barlat, F.; Lee, M.G. Springback reduction in tailor welded blank with high strength differential by using multi-objective evolutionary and genetic algorithms. Steel Research International 2015, 86, 1391–1402.
   Web of Science ®Google Scholar
• Pettersson, F.; Chakraborti, N.; Saxen, H. A genetic algorithms based multi-objective neural net applied to noisy blast furnace data. Applied Soft Computing 2007, 7, 387–397.
   Web of Science ®Google Scholar
• Chakraborti, N. Strategies for Evolutionary Data Driven Modeling in Chemical and Metallurgical Systems. Springer International Publishing: Switzerland, 2014.
   Google Scholar
• Chakraborti, N. Strategies for evolutionary data driven modeling in chemical and metallurgical Systems. In Applications of Metaheuristics in Process Engineering. Springer International Publishing; 2014, 89–122.
   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, 112–23.
   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, 2613–2623.
   Web of Science ®Google Scholar
• Giri, B.K.; Pettersson, F.; Saxén, H.; Chakraborti, N. Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives. Materials and Manufacturing Processes 2013, 28 (7), 776–782.
   Web of Science ®Google Scholar
• Chakraborti, N. Data-driven bi-objective genetic algorithms EvoNN and BioGP and their applications in metallurgical and materials domain. Computational Approaches to Materials Design: Theoretical and Practical Aspects: Theoretical and Practical Aspects 2016, 346.
   Google Scholar
• Iacca, G.; Mininno, E. Introducing kimeme, a novel platform for multi-disciplinary multi-objective optimization. In Advances in Artificial Life, Evolutionary Computation and Systems Chemistry. Springer International Publishing, 2015; 40–52.
   Google Scholar
• Jha, R.; Dulikravich, G.S.; Chakraborti, N.; Fan, M.; Schwartz, J.; Koch, C.C.; Colaco, M.J.;.; Poloni, C.; Egorov, I.N. Algorithms for design optimization of chemistry of hard magnetic alloys using experimental data. Journal of Alloys and Compounds 2016, 682, 454–467.
   Web of Science ®Google Scholar
• Nguyen, T.N.; Siegmund, T.; Tsutsui, W.; Liao, H.J.; Chen, W. Bi-objective optimal design of a damage-tolerant multifunctional battery system. Materials & Design 2016, 105, 51–65.
   Web of Science ®Google Scholar
• Mohanty, K.; Roy, G.G.; Chakraborti, N. Simulation and meta-modeling of electron beam welding using genetic algorithms, Metallurgia Italiana 2016, 3, 45–48.
   Google Scholar
• Goyal, H.; Mandal, N.; Roy, H.; Mitra, S.K.; Mondal, B. Multi response optimization for processing Al-SiCp composites: An approach towards enhancement of mechanical properties. Transactions of the Indian Institute of Metals 2015, 68, 453–463.
   Web of Science ®Google Scholar
• Hariharan, K.; Ngoc-Trung Nguyen, N.; Chakraborti, N.; Barlat, F.; Lee, M.G. Determination of anisotropic yield coefficients by a data-driven multiobjective evolutionary and genetic algorithm. Materials and Manufacturing Processes 2015, 30, 403–413.
   Web of Science ®Google Scholar
• Halder, C.; Madej, L.; Pietrzyk, M.; Chakraborti, N. Optimization of cellular automata model for the heating of dual-phase steel by genetic algorithm and genetic programming. Materials and Manufacturing Processes 2015, 30, 552–562.
   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
• Bevilacqu, V.; Nuzzolese, N.; Mininno, E.; Iacca, G. Adaptive bi-objective genetic programming for data-driven system modeling, intelligent computing methodologies. Series Lecture Notes in Computer Science 2016, 9773, 248–259.
   Google Scholar
• Li, X. A real-coded predator–prey genetic algorithm for multi-objective optimization In Proceedings of the Second International Conference on Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science; Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L., Eds.; Springer International Publishing, 2003, 2632, 207–221.
   Google Scholar
• Coello, C.A.C.; Van Veldhuizen, D.A.; Lamont, G.B. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic: New York, 2002.
   Google Scholar
• Hariharan, K.; Chakraborti, N.; Barlat, F.; Lee, M.G. A novel multi-objective genetic algorithms-based calculation of Hill’s coefficients. Metallurgical and Materials Transactions A 2014, 45A, 2704–2707.
   Web of Science ®Google Scholar
• Halder, C.; Karmakar, A.; Hasan, Sk. Md.; Chakrabarti, D.; Pietrzyk, M.; Chakraborti, N. Effect of carbon distribution during the microstructure evolution of dual phase steels studied using cellular automata, genetic algorithms and experimental strategies. Metallurgical and Materials Transactions A 2016 (In Press).
   Web of Science ®Google Scholar
• Mohanty, K.; Mitra, T.; Saxén, H.; Chakraborti, N. Multiple criteria in a top gas recycling blast furnace optimized through a k-optimality-based genetic algorithm. Steel Research International 2016, 87, 1284–1294.
   Web of Science ®Google Scholar
• Krauss, G. Steels: Processing, Structure, and Performance, 2 edn, ASM International: 9639 Kinsman Road, Materials Park, United States, 2015; OH 44073–0002, p. 30.
   Google Scholar
• Gorni, A.A. Steel Forming and Heat Treating Handbook. Sao Vicente, 2012, p. 1.
   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, 2502–2516.
   Web of Science ®Google Scholar
• Govindan, D.; Chakraborty, S.; Chakraborti, N. Analyzing the fluid flow in continuous casting through evolutionary neural nets and multi-objective genetic algorithms. Steel Research International 2010, 81, 197–203.
   Web of Science ®Google Scholar
• Helle, M.; Pettersson, F.; Chakraborti, N.; Saxen, H. Modelling noisy blast furnace data using genetic algorithms and neural networks. Steel Research International 2006, 77, 75–81.
   Web of Science ®Google Scholar
• Karmakar, A.; Ghosh, M.; Chakrabarti, D. Cold-rolling and inter-critical annealing of low-carbon steel: effect of initial microstructure and heating-rate. Materials Science and Engineering: A 2013, 564, 389–399.
   Web of Science ®Google Scholar
• Karmakar, A.; Sivaprasad, S.; Kundu, S.; Chakrabarti, D. Tensile behavior of ferrite-carbide and ferrite-martensite steels with different ferrite grain structures. Metallurgical and Materials Transactions A 2014, 45 (4), 1659–1664.
   Web of Science ®Google Scholar
• Chakraborti, N. Critical assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research. Materials Science and Technology 2014, 30, 1259–1262.
   Web of Science ®Google Scholar
• Chakraborti, N. Promise of multi objective genetic algorithms in coating performance formulation. Surface Engineering 2014, 30, 79–82.
   Web of Science ®Google Scholar