Subhas  Ganguly

Department Metallurgical Engineering
Designation Assistant Professor
Educational Qualification PhD
Contact Number 9433396665
Areas of Interest

Phase Transformation, Alloy Design, Multicomponent System, Computational Materials Science, Engineering Optimization, Genetic Algorithm, Soft Computing

  1. A predictable glass forming ability expression by statistical learning and evolutionary intelligence, MK Tripathi, PP Chattopadhyay, S Ganguly, Intermetallics 90, 9-15, 2017
  2. Evolutionary Intelligence in Design and Synthesis of Bulk Metallic Glasses by Mechanical Alloying , MK Tripathi, PP Chattopadhyay, S Ganguly, Materials and Manufacturing Processes, 1-8, 2017
  3. New training strategies for neural networks with application to quaternary Al–Mg–Sc–Cr alloy design problems, S Ganguly, A Patra, PP Chattopadhyay, S Datta, Applied soft computing 46, 260-266, 2016
  4. Modeling of Steelmaking Processes, S Louhenkilpi, S Ganguly, Computational Approaches to Materials Design: Theoretical and Practical ... 2016
  5. Evolution of glass forming ability indicator by genetic programming, MK Tripathi, S Ganguly, P Dey, PP Chattopadhyay, Computational Materials Science 118, 56-65, 2016
  6. Imprecise Knowledge and Fuzzy Modeling in Materials Domain, S Ganguly, S Datta
  7. Computational Approaches to Materials Design: Theoretical and Practical ...,,  2016
  8. Computational design and development of novel Al-Mg-Sc-Cr alloy, A Patra, S Ganguly, PP Chattopadhyay, S Datta, Multidiscipline Modeling in Materials and Structures 11 (3), 401-412, 2015
  9. Multivariate analysis and classification of bulk metallic glasses using principal component analysis, MK Tripathi, PP Chattopadhyay, S Ganguly, Computational Materials Science 107, 79-87, 2015
  10. In silico Design of High Strength Aluminium Alloy Using Multi-objective GA, S Dey, S Ganguly, S Datta, International Conference on Swarm, Evolutionary, and Memetic Computing, 316-327, 2014
  11. Informatics-based uncertainty quantification in the design of inorganic scintillators, S Ganguly, CS Kong, SR Broderick, K Rajan, Materials and Manufacturing Processes 28 (7), 726-732, 2013
  12. Nano-intermetallic precipitated Al-based amorphous matrix composite design by artificial neural network analysis, S Ganguly, OA Ojo, PP Chattopadhyay, D Roy, Journal of Materials Science Research 1 (3), 59, 2012
  13. Investigating the role of metallic fillers in particulate reinforced flexible mould material composites using evolutionary algorithms, AK Nandi, K Deb, S Ganguly, S Datta, Applied Soft Computing 12 (1), 28-39, 2012
  14. Effect of quaternary zirconium addition on mechanical properties of Al-6Mg-Sc (0.2-0.6%) alloy studied by ANN technique,A Patra, S Ganguly, MS Kaiser, PP Chattopadhyay, S Datta,International Journal of Mechatronics and Manufacturing Systems 3 (1-2), 144-154, 2009
  15. Genetic algorithm based optimization for multi-physical properties of HSLA steel through hybridization of neural network and desirability function, P Das, S Mukherjee, S Ganguly, BK Bhattacharyay, S Datta, Computational Materials Science 45 (1), 104-110, 2009
  16. Genetic algorithm based search of parameters for fabrication of uniform porous silicon nanostructure, M Ray, S Ganguly, M Das, SM Hossain, NR Bandyopadhyay, Computational Materials Science 45 (1), 60-64, 2009
  17. Genetic algorithm-based search on the role of variables in the work hardening process of multiphase steels, S Ganguly, S Datta, N Chakraborti
  18. Computational Materials Science 45 (1), 158-166, 2009
  19. Effect of copper and microalloying (Ti, B) addition on tensile properties of HSLA steels predicted by ANN technique ,SK Ghosh, S Ganguly, PP Chattopadhyay, S Datta, Ironmaking & Steelmaking 36 (2), 125-132, 2009
  20. Simulating time temperature transformation diagram of steel using artificial neural network, M Kundu, S Ganguly, S Datta, PP Chattopadhyay, Materials and Manufacturing Processes 24 (2), 169-173, 2009
  21. ANN 技術で予測した HSLA 鋼の引張特性に及ぼす銅および微量合金 (Ti, B) 添加の影響, SK GHOSH, S GANGULY, PP CHATTOPADHYAY, S DATTA, Ironmak Steelmak 36 (2), 125-132, 2009
  22. Development of High Manganese Steel for Thin Gauge Application Using ANN, N Bhowmik, S Ganguly, Journal of IE (I), MM Division 90, 3-8, 2009
  23. Exploring the possibilities of development of directly quenched TRIP-aided steel by the artificial neural networks (ANN) technique, KP Das, S Ganguly, PP Chattopadhyay, S Tarafder, NR Bandyopadhyay, Materials and Manufacturing Processes 24 (1), 68-77, 2008
  24. Artificial neural network (ANN)-based model for in situ prediction of porosity of nanostructured porous silicon, M Ray, S Ganguly, M Das, S Datta, NR Bandyopadhyay, SM Hossain, Materials and Manufacturing Processes 24 (1), 83-87, 2008
  25. Designing the multiphase microstructure of steel for optimal TRIP effect: A multiobjective genetic algorithm based approach, S Ganguly, S Datta, PP Chattopadhyay, N Chakraborti, Materials and Manufacturing Processes 24 (1), 31-37, 2008
  26. Development of High-Strength Cu-Ni-Ti-B Multiphase Steel by Direct Air Cooling, SK Ghosh, A Haldar, S Ganguly, PP Chattopadhyay, Metallurgical and Materials Transactions A 39 (11), 2555-2568, 2008
  27. Design of the directly air-cooled pearlite-free multiphase steel from CCT diagrams developed using ANN and dilatometric methods, SK Ghosh, PP Chattopadhyay, A Haldar, S Ganguly, S Datta, ISIJ international 48 (5), 649-657, 2008
  28. Identification of factors governing mechanical properties of TRIP-aided steel using genetic algorithms and neural networks, S Datta, F Pettersson, S Ganguly, H Saxen, N Chakraborti, Materials and Manufacturing Processes 23 (2), 130-137, 2008
  29. Determination of Ms Temperature in Copper-Bearing Microalloyed Steel by the ANN Technique, SK Ghosh, S Ganguly, PP Chattopadhyay, S Datta, Canadian Metallurgical Quarterly 47 (1), 91-98, 2008
  30. Genetic algorithms in optimization of strength and ductility of low-carbon steels, S Ganguly, S Datta, N Chakraborti, Materials and Manufacturing Processes 22 (5), 650-658, 2007
  31. Designing high strength multi-phase steel for improved strength–ductility balance using neural networks and multi-objective genetic algorithms, S Datta, F Pettersson, S Ganguly, H Saxén, N Chakraborti, ISIJ international 47 (8), 1195-1203, 2007
  32. Modeling the effect of copper on hardness of microalloyed dual phase steel through neural network and neuro-fuzzy systems, SK Ghosh, S Ganguly, PP Chattopadhyay, S Datta, ISIJ international 45 (9), 1345-1351, 2005
Other Info.

Awards and Achievement

1. Young Engineers Award 2008 MME Division, Institution of Engineers India.

2. Best paper 2010, MME Division, Institution of Engineeris India

3. Visiting Scientist, Department of Materials Science and Engineering, Iowa  State University Iowa, USA. 2010

4 Visiting post doctoral fellow, University of Manitoba, Manitoba canada. 2009


Sponsored R&D Projects

1. Study of ageing behaviour of Cu-added austenitic grade stainless steel and modeling of ageing characterstics. IE(I) Research grant, Ref. Project ID UG2014039