| Prediction of flow stress in Ti–6Al–4V alloy with an equiaxed α+ β microstructure by artificial neural networks NS Reddy, YH Lee, CH Park, CS Lee Materials Science and Engineering: A 492 (1-2), 276-282, 2008 | 128 | 2008 |
| Prediction of grain size of Al–7Si Alloy by neural networks NS Reddy, AKP Rao, M Chakraborty, BS Murty Materials Science and Engineering: A 391 (1-2), 131-140, 2005 | 99 | 2005 |
| Flow softening behavior during high temperature deformation of AZ31Mg alloy BH Lee, NS Reddy, JT Yeom, CS Lee Journal of Materials Processing Technology 187, 766-769, 2007 | 98 | 2007 |
| Modeling medium carbon steels by using artificial neural networks NS Reddy, J Krishnaiah, SG Hong, JS Lee Materials Science and Engineering: A 508 (1-2), 93-105, 2009 | 82 | 2009 |
| Tensile properties of a newly developed high-temperature titanium alloy at room temperature and 650 C PL Narayana, SW Kim, JK Hong, NS Reddy, JT Yeom Materials Science and Engineering: A 718, 287-291, 2018 | 76 | 2018 |
| Silica-polymer hybrid materials as methylene blue adsorbents HS Jamwal, S Kumari, GS Chauhan, NS Reddy, JH Ahn Journal of environmental chemical engineering 5 (1), 103-113, 2017 | 61 | 2017 |
| Microstructural response of β-stabilized Ti–6Al–4V manufactured by direct energy deposition PL Narayana, S Lee, SW Choi, CL Li, CH Park, JT Yeom, NS Reddy, ... Journal of Alloys and Compounds 811, 152021, 2019 | 57 | 2019 |
| Artificial neural network modeling on the relative importance of alloying elements and heat treatment temperature to the stability of α and β phase in titanium alloys NS Reddy, BB Panigrahi, CM Ho, JH Kim, CS Lee Computational Materials Science 107, 175-183, 2015 | 56 | 2015 |
| Modeling hot deformation behavior of low-cost Ti-2Al-9.2 Mo-2Fe beta titanium alloy using a deep neural network CL Li, PL Narayana, NS Reddy, SW Choi, JT Yeom, JK Hong, CH Park Journal of Materials Science & Technology 35 (5), 907-916, 2019 | 52 | 2019 |
| Design of medium carbon steels by computational intelligence techniques NS Reddy, J Krishnaiah, HB Young, JS Lee Computational Materials Science 101, 120-126, 2015 | 50 | 2015 |
| Predictive capability evaluation and optimization of Pb (II) removal by reduced graphene oxide-based inverse spinel nickel ferrite nanocomposite PL Narayana, LP Lingamdinne, RR Karri, S Devanesan, MS AlSalhi, ... Environmental Research 204, 112029, 2022 | 47 | 2022 |
| The role of artificial neural networks in prediction of mechanical and tribological properties of composites—a comprehensive review UMR Paturi, S Cheruku, NS Reddy Archives of Computational Methods in Engineering 29 (5), 3109-3149, 2022 | 40 | 2022 |
| Modeling high-temperature mechanical properties of austenitic stainless steels by neural networks PL Narayana, SW Lee, CH Park, JT Yeom, JK Hong, AK Maurya, ... Computational Materials Science 179, 109617, 2020 | 39 | 2020 |
| Determination of the beta-approach curve and beta-transus temperature for titanium alloys using sensitivity analysis of a trained neural network NS Reddy, CS Lee, JH Kim, SL Semiatin Materials Science and Engineering: A 434 (1-2), 218-226, 2006 | 38 | 2006 |
| High strength and ductility of electron beam melted β stabilized γ-TiAl alloy at 800 C PL Narayana, CL Li, SW Kim, SE Kim, A Marquardt, C Leyens, NS Reddy, ... Materials Science and Engineering: A 756, 41-45, 2019 | 37 | 2019 |
| The role of machine learning in tribology: a systematic review UMR Paturi, ST Palakurthy, NS Reddy Archives of Computational Methods in Engineering 30 (2), 1345-1397, 2023 | 34 | 2023 |
| Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining UMR Paturi, S Cheruku, VPK Pasunuri, S Salike, NS Reddy, S Cheruku Machine Learning with Applications 6, 100099, 2021 | 33 | 2021 |
| High temperature deformation behavior of Ti− 6Al− 4V alloy with and equiaxed microstructure: a neural networks analysis NS Reddy, YH Lee, JH Kim, CS Lee Metals and Materials International 14, 213-221, 2008 | 33 | 2008 |
| Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels D Kim, J Lee, MS Lee, HJ Son, NS Reddy, M Kim, SK Moon, KT Kim, ... Materialia 11, 100699, 2020 | 32 | 2020 |
| Microstructure prediction of two-phase titanium alloy during hot forging using artificial neural networks and FE simulation JH Kim, NS Reddy, JT Yeom, JK Hong, CS Lee, NK Park Metals and Materials International 15, 427-437, 2009 | 30 | 2009 |
Jae-keun HongKIMSVerified email at kims.re.kr
