Analyses of anti-wear and extreme pressure properties of castor oil with zinc oxide nano friction modifiers S Bhaumik, R Maggirwar, S Datta, SD Pathak Applied Surface Science 449, 277-286, 2018 | 127 | 2018 |
Soft computing techniques in avdancement of structural metals S Datta, PP Chattopadhyay International Materials Review, 2013 | 101 | 2013 |
Design of patient specific dental implant using FE analysis and computational intelligence techniques S Roy, S Dey, N Khutia, AR Chowdhury, S Datta Applied soft computing 65, 272-279, 2018 | 92 | 2018 |
Effect of graphene on the properties of flax fabric reinforced epoxy composites M Kamaraj, EA Dodson, S Datta Advanced Composite Materials 29 (5), 443-458, 2020 | 72 | 2020 |
Designing cold rolled IF steel sheets with optimized tensile properties using ANN and GA I Mohanty, D Bhattacharjee, S Datta Computational materials science 50 (8), 2331-2337, 2011 | 70 | 2011 |
Effect of scandium on the microstructure and ageing behaviour of cast Al–6Mg alloy MS Kaiser, S Datta, A Roychowdhury, MK Banerjee Materials Characterization 59 (11), 1661-1666, 2008 | 67 | 2008 |
Designing dual-phase steels with improved performance using ANN and GA in tandem T Dutta, S Dey, S Datta, D Das Computational Materials Science 157, 6-16, 2019 | 63 | 2019 |
Artificial intelligence based design of multiple friction modifiers dispersed castor oil and evaluating its tribological properties S Bhaumik, SD Pathak, S Dey, S Datta Tribology International 140, 105813, 2019 | 61 | 2019 |
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 | 59 | 2007 |
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 | 57 | 2007 |
Computational intelligence-based design of lubricant with vegetable oil blend and various nano friction modifiers S Bhaumik, BR Mathew, S Datta Fuel 241, 733-743, 2019 | 56 | 2019 |
Exploring the effects of chemical composition in hot rolled steel product using Mahalanobis distance scale under Mahalanobis–Taguchi system P Das, S Datta Computational Materials Science 38 (4), 671-677, 2007 | 52 | 2007 |
Computational intelligence based design of age-hardenable aluminium alloys for different temperature regimes S Dey, N Sultana, MS Kaiser, P Dey, S Datta Materials & Design 92, 522-534, 2016 | 49 | 2016 |
Incorporation of prior knowledge in neural network model for continuous cooling of steel using genetic algorithm S Chakraborty, PP Chattopadhyay, SK Ghosh, S Datta Applied Soft Computing 58, 297-306, 2017 | 48 | 2017 |
Analyses of tribological properties of castor oil with various carbonaceous micro-and nano-friction modifiers S Bhaumik, S Datta, SD Pathak Journal of Tribology 139 (6), 061802, 2017 | 47 | 2017 |
Computational intelligence based designing of microalloyed pipeline steel S Pattanayak, S Dey, S Chatterjee, SG Chowdhury, S Datta Computational Materials Science 104, 60-68, 2015 | 46 | 2015 |
Design of the ultrahigh molecular weight polyethylene composites with multiple nanoparticles: An artificial intelligence approach A Vinoth, S Datta Journal of Composite Materials 54 (2), 179-192, 2020 | 44 | 2020 |
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 | 44 | 2009 |
Effect of thermomechanical processing on the microstructure and properties of a low carbon copper bearing steel MK Banerjee, PS Banerjee, S Datta ISIJ international 41 (3), 257-261, 2001 | 43 | 2001 |
Genetic algorithm-based search on the role of variables in the work hardening process of multiphase steels S Ganguly, S Datta, N Chakraborti Computational Materials Science 45 (1), 158-166, 2009 | 42 | 2009 |