| 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 | 136 | 2008 |
| Colonic diversion for intractable constipation in children: colonic manometry helps guide clinical decisions J Villarreal, M Sood, T Zangen, A Flores, R Michel, N Reddy, ... Journal of pediatric gastroenterology and nutrition 33 (5), 588-591, 2001 | 110 | 2001 |
| 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 | 101 | 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 | 89 | 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 | 85 | 2018 |
| 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 | 72 | 2023 |
| 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 | 71 | 2019 |
| 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 | 66 | 2017 |
| 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 | 64 | 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 | 63 | 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 | 60 | 2022 |
| Design of medium carbon steels by computational intelligence techniques NS Reddy, J Krishnaiah, HB Young, JS Lee Computational Materials Science 101, 120-126, 2015 | 57 | 2015 |
| 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 | 55 | 2021 |
| 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 | 53 | 2019 |
| 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 | 49 | 2020 |
| 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 | 48 | 2019 |
| Optimization of hybrid manufacturing process combining forging and wire-arc additive manufactured Ti-6Al-4V through hot deformation characterization AK Maurya, JT Yeom, SW Kang, CH Park, JK Hong, NS Reddy Journal of Alloys and Compounds 894, 162453, 2022 | 46 | 2022 |
| Modeling the relationship between electrospinning process parameters and ferrofluid/polyvinyl alcohol magnetic nanofiber diameter by artificial neural networks AK Maurya, PL Narayana, AG Bhavani, H Jae-Keun, JT Yeom, NS Reddy Journal of Electrostatics 104, 103425, 2020 | 38 | 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 |
Jae-keun HongKIMSVerified email at kims.re.kr
