Personal information

Other IDs

SciProfiles: 959673
Scopus Author ID: 14424537300

Keywords

Artificial Neural Networks;, Structure property Relationships;, Machine Learning;, Ti alloys;, Steels;, Entrepreneurial Marketing;, Sustainability;

Countries

South Korea, India

Activities

Collapse all

Employment (4)

Gyeongsang National University: Jinju, Gyeongsangnam-do, KR

2020-09-01 to present | Professor (School of Materials Science and Engineering)
Employment
Source: Self-asserted source
N. S. Reddy

Gyeongsang National University: Jinju, KR

2015-09-01 to 2020-08-31 | Associate Professor (School of Materials Science and Engineering)
Employment
Source: Self-asserted source
N. S. Reddy

Gyeongsang National University: Jinju, KR

2010-03-01 to 2015-08-31 | Assistant Professor (School of Materials Science and Engineering)
Employment
Source: Self-asserted source
N. S. Reddy

Pohang University of Science and Technology: Pohang, Gyeongsangbuk-do, KR

2004-08-24 to 2010-03-01 | Postdoctoral fellow/ Research Scientist (Materials Science and Engineering)
Employment
Source: Self-asserted source
N. S. Reddy

Education and qualifications (4)

Indian Institute of Technology Kharagpur: Kharagpur, West Bengal, IN

1999-04-01 to 2004-07-28 | Ph.D. (Metallurgical & Materials Engineering)
Education
Source: Self-asserted source
N. S. Reddy

Indian Institute of Technology Kharagpur: Kharagpur, West Bengal, IN

1997-07 to 1999-01 | M. Tech (Metallurgical Engineering)
Education
Source: Self-asserted source
N. S. Reddy

Institution of Engineers India: Kolkata, West Bengal, IN

1992-06 to 1994-12 | A. M. I. E (Mechanical Engineering)
Education
Source: Self-asserted source
N. S. Reddy

Governament Polytechnic College: Anantapur, Andhra Pradesh, IN

1987-07 to 1991-02 | Diploma in Automobile Engineering (Automobile Engineering)
Education
Source: Self-asserted source
N. S. Reddy

Works (50 of 105)

Items per page:
Page 1 of 3

Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys

2025-03-27 | Preprint
DOI: 10.20944/preprints202503.2087.v1
Contributors: Uma Maheshwera Reddy Paturi; Muhammad Ishtiaq; Pasupuleti Lakshmi Narayana; Anoop Kumar Maurya; Seong Woo Choi; Nagireddy Gari Subba Reddy
Source: check_circle
Crossref

Prediction of Creep Rupture Life of 5Cr-0.5Mo Steel Using Machine Learning Models

Metals
2025-03-06 | Journal article
DOI: 10.3390/met15030288
Contributors: Muhammad Ishtiaq; Hafiz Muhammad Rehan Tariq; Devarapalli Yuva Charan Reddy; Sung-Gyu Kang; Nagireddy Gari Subba Reddy
Source: check_circle
Crossref
grade
Preferred source (of 2)‎

Engineered Pt-Pd@RGO-KI nanosheet catalyst for enhanced electrocatalytic methanol oxidation performance

Molecular Catalysis
2025-02 | Journal article
DOI: 10.1016/j.mcat.2024.114771
Contributors: Swathi Challa; Chinna Gangi Reddy Nallagondu; Rakesh Kulkarni; Sreenivasa Kumar Godlaveeti; Chandra Sekhar Yellatur; Subramanyam Sarma Loka; N.S. Reddy; Janardhan Reddy Koduru; Adinarayana Reddy Somala
Source: check_circle
Crossref

Artificial Neural Network Modeling of Ti-6Al-4V Alloys to Correlate Their Microstructure and Mechanical Properties

Materials
2025-02-28 | Journal article
DOI: 10.3390/ma18051099
Contributors: Anoop Kumar Maurya; Pasupuleti Lakshmi Narayana; Jong-Taek Yeom; Jae-Keun Hong; Nagireddy Gari Subba Reddy
Source: check_circle
Crossref
grade
Preferred source (of 2)‎

Data-Driven ANN-Based Predictive Modeling of Mechanical Properties of 5Cr-0.5Mo Steel: Impact of Composition and Service Temperature

Crystals
2025-02-24 | Journal article
DOI: 10.3390/cryst15030213
Contributors: Muhammad Ishtiaq; Saurabh Tiwari; Molakatala Nagamani; Sung-Gyu Kang; Nagireddy Gari Subba Reddy
Source: check_circle
Crossref
grade
Preferred source (of 2)‎

Knowledge Discovery in Predicting Martensite Start Temperature of Medium-Carbon Steels by Artificial Neural Networks

Algorithms
2025-02-19 | Journal article
DOI: 10.3390/a18020116
Contributors: Xiao-Song Wang; Anoop Kumar Maurya; Muhammad Ishtiaq; Sung-Gyu Kang; Nagireddy Gari Subba Reddy
Source: check_circle
Crossref
grade
Preferred source (of 2)‎

Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks

Batteries
2024-12-31 | Journal article
DOI: 10.3390/batteries11010013
Contributors: Mookala Premasudha; Bhumi Reddy Srinivasulu Reddy; Kwon-Koo Cho; Ahn Hyo-Jun; Jae-Kyung Sung; Nagireddy Gari Subba Reddy
Source: check_circle
Crossref
grade
Preferred source (of 2)‎

Statistical and Electrochemical Insights of Hexagonal NiCoMg-LDH Nanosheets toward Overall Water Splitting and Methanol Oxidation Reactions

ACS Applied Energy Materials
2024-11-25 | Journal article
DOI: 10.1021/acsaem.4c02144
Contributors: Rakesh Kulkarni; Swapnil R. Patil; Lakshmi Prasanna Lingamdinne; Santosh S. Sutar; Chandrika Ashwinikumar Pal; N. S. Reddy; Yoon-Young Chang; Jinho Bae; Janardhan Reddy Koduru
Source: check_circle
Crossref

Artificial neural network model for extracting knowledge from the electro‐Fenton process for acid mine wastewater treatment

CLEAN – Soil, Air, Water
2024-10 | Journal article
DOI: 10.1002/clen.202400029
Contributors: Anoop Kumar Maurya; Pasupuleti Lakshmi Narayana; Uma Maheshwera Reddy Paturi; Subba Reddy Nagireddy Gari
Source: check_circle
Crossref

Stabilizing the Solid Electrolyte Interface Using a Zero-Strain Feature Protective Layer for a High-Performance Silicon–Graphite Anode

Energy & Fuels
2024-09-19 | Journal article
DOI: 10.1021/acs.energyfuels.4c01846
Contributors: S. Jayasubramaniyan; Yoonkwang Lee; Jueun Kim; Seokjin Kim; Donghwi Kim; Minseok Ko; N. S. Reddy; Sang Yong Nam; Jaekyung Sung
Source: check_circle
Crossref

Surface fluorinated graphite suppressing the lithium dendrite formation for fast chargeable lithium ion batteries

Carbon
2024-02 | Journal article
DOI: 10.1016/j.carbon.2024.118808
Contributors: Minseok Ko; S. Jayasubramaniyan; Seokjin Kim; Jueun Kim; Donghwi Kim; N.S. Reddy; Hyunsoo Ma; Sang Yong Nam; Jaekyung Sung
Source: check_circle
Crossref

Modeling the adsorption process for fluoride removal from groundwater by machine learning

Environmental Progress & Sustainable Energy
2023-11 | Journal article
DOI: 10.1002/ep.14221
Contributors: B. S. Reddy; A. K. Maurya; Hong Hyeon‐A; Tae‐Hui Lee; K. K. Cho; N. S. Reddy
Source: check_circle
Crossref

Estimation of surface roughness of direct metal laser sintered AlSi10Mg using artificial neural networks and response surface methodology

Materials and Manufacturing Processes
2023-10-26 | Journal article
DOI: 10.1080/10426914.2023.2217890
Contributors: Uma Maheshwera Reddy Paturi; Dheeraj Goud Vanga; Rennie Bowen Duggem; Nitin Kotkunde; N.S. Reddy; Sunil Dutta
Source: check_circle
Crossref

Neural Network Models for Estimating the Impact of Physicochemical and Operational Parameters on the Specific Capacity of Activated-Carbon-Based Supercapacitors

Energy & Fuels
2023-10-05 | Journal article
DOI: 10.1021/acs.energyfuels.3c01906
Contributors: B. S. Reddy; A. K. Maurya; Uma Maheshwera Reddy Paturi; Jaekyung Sung; P. L. Narayana; Hyo-Jun Ahn; K. K. Cho; N. S. Reddy
Source: check_circle
Crossref

Modeling the Nonlinearities Between Coaching Leadership and Turnover Intention by Artificial Neural Networks

Sage Open
2022-10 | Journal article
DOI: 10.1177/21582440221126885
Contributors: Won Seok Bang; Wee Kuk Hoan; Ju Young Park; Nagireddy gari Subba Reddy
Source: check_circle
Crossref

Predictive capability evaluation and optimization of Pb(II) removal by reduced graphene oxide-based inverse spinel nickel ferrite nanocomposite

Environmental Research
2022-03 | Journal article
DOI: 10.1016/j.envres.2021.112029
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

High temperature isothermal oxidation behavior of electron beam melted multi-phase $̆pgamma$-TiAl alloy

Intermetallics
2022-02 | Journal article
DOI: 10.1016/j.intermet.2021.107424
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

Development of artificial neural networks software for arsenic adsorption from an aqueous environment

Environmental Research
2022-01 | Journal article
DOI: 10.1016/j.envres.2021.111846
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

Estimation of machinability performance in wire-EDM on titanium alloy using neural networks

Materials and Manufacturing Processes
2022-01 | Journal article
DOI: 10.1080/10426914.2022.2030875
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

Modeling of surface roughness in wire electrical discharge machining of Inconel 718 using artificial neural network

Materials Today: Proceedings
2021 | Journal article
DOI: 10.1016/j.matpr.2020.09.503
Contributors: Uma Maheshwera Reddy Paturi; Harish Devarasetti; N.S. Reddy; Nitin Kotkunde; B.K. Patle
Source: check_circle
Crossref

Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining

Machine Learning with Applications
2021-12 | Journal article
DOI: 10.1016/j.mlwa.2021.100099
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

Egg white derived carbon materials as an efficient sulfur host for high-performance lithium-sulfur batteries and its electrochemical properties

Materials Research Bulletin
2021-08 | Journal article
DOI: 10.1016/j.materresbull.2021.111310
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

Modeling the teacher job satisfaction by artificial neural networks

Soft Computing
2021-06 | Journal article
DOI: 10.1007/s00500-021-05958-0
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

Journal of Applied Polymer Science
2021-05 | Journal article
DOI: 10.1002/app.50956
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

Quantitative estimation of corrosion rate in 3C steels under seawater environment

Journal of Materials Research and Technology
2021-03 | Journal article
DOI: 10.1016/j.jmrt.2021.01.039
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

Modeling mechanical properties of 25Cr-20Ni-0.4C steels over a wide range of temperatures by neural networks

Metals
2020 | Journal article
DOI: 10.3390/met10020256
EID:

2-s2.0-85079715822
Contributors: Narayana, P.L.; Kim, J.H.; Maurya, A.K.; Park, C.H.; Hong, J.-K.; Yeom, J.-T.; Reddy, N.S.
Source: Self-asserted source
N. S. Reddy via Scopus - Elsevier
grade
Preferred source (of 2)‎

Modeling mechanical properties of 25Cr-20Ni-0.4C steels over a wide range of temperatures by neural networks

Metals
2020 | Journal article
Source: Self-asserted source
N. S. Reddy

Modeling the relationship between electrospinning process parameters and ferrofluid/polyvinyl alcohol magnetic nanofiber diameter by artificial neural networks

Journal of Electrostatics
2020 | Journal article
DOI: 10.1016/j.elstat.2020.103425
EID:

2-s2.0-85079351660
Contributors: Maurya, A.K.; Narayana, P.L.; Bhavani, A.G.; Jae-Keun, H.; Yeom, J.-T.; Reddy, N.S.
Source: Self-asserted source
N. S. Reddy via Scopus - Elsevier
grade
Preferred source (of 2)‎

Modeling the relationship between electrospinning process parameters and ferrofluid/polyvinyl alcohol magnetic nanofiber diameter by artificial neural networks

Journal of Electrostatics
2020 | Journal article
Source: Self-asserted source
N. S. Reddy

Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon $łess$scp$\greater$VII$łess$/scp$\greater$ starch) nanofiber diameter

Journal of Applied Polymer Science
2020-10 | Journal article
DOI: 10.1002/app.50014
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels

Materialia
2020-06 | Journal article
DOI: 10.1016/j.mtla.2020.100699
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 2)‎

Modeling high-temperature mechanical properties of austenitic stainless steels by neural networks

Computational Materials Science
2020-06 | Journal article
DOI: 10.1016/j.commatsci.2020.109617
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 3)‎

Modeling constituent–property relationship of polyvinylchloride composites by neural networks

Polymer Composites
2020-04 | Journal article
DOI: 10.1002/pc.25612
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 3)‎

Characterization of Hot Deformation Behavior and Processing Maps of Ti?19Al?22Mo Alloy

Metals and Materials International
2019 | Journal article
Source: Self-asserted source
N. S. Reddy

Characterization of Hot Deformation Behavior and Processing Maps of Ti–19Al–22Mo Alloy

Metals and Materials International
2019 | Journal article
DOI: 10.1007/s12540-018-00237-4
EID:

2-s2.0-85061066824
Contributors: Narayana, P.L.; Li, C.-L.; Hong, J.-K.; Choi, S.-W.; Park, C.H.; Kim, S.-W.; Kim, S.E.; Reddy, N.S.; Yeom, J.-T.
Source: Self-asserted source
N. S. Reddy via Scopus - Elsevier
grade
Preferred source (of 2)‎

Correction to: Characterization of Hot Deformation Behavior and Processing Maps of Ti?19Al?22Mo Alloy (Metals and Materials International, (2019), 25, 4, (1063-1071), 10.1007/s12540-018-00237-4)

Metals and Materials International
2019 | Journal article
Source: Self-asserted source
N. S. Reddy

Correction to: Characterization of Hot Deformation Behavior and Processing Maps of Ti–19Al–22Mo Alloy (Metals and Materials International, (2019), 25, 4, (1063-1071), 10.1007/s12540-018-00237-4)

Metals and Materials International
2019 | Journal article
DOI: 10.1007/s12540-019-00352-w
EID:

2-s2.0-85069531130
Contributors: Narayana, P.L.; Li, C.-L.; Hong, J.-K.; Choi, S.-W.; Park, C.H.; Kim, S.-W.; Kim, S.E.; Reddy, N.S.; Yeom, J.-T.
Source: Self-asserted source
N. S. Reddy via Scopus - Elsevier
grade
Preferred source (of 2)‎

High strength and ductility of electron beam melted ? stabilized ?-TiAl alloy at 800?C

Materials Science and Engineering A
2019 | Journal article
Source: Self-asserted source
N. S. Reddy

High strength and ductility of electron beam melted β stabilized γ-TiAl alloy at 800°C

Materials Science and Engineering A
2019 | Journal article
DOI: 10.1016/j.msea.2019.03.114
EID:

2-s2.0-85064139501
Contributors: Narayana, P.L.; Li, C.-L.; Kim, S.-W.; Kim, S.-E.; Marquardt, A.; Leyens, C.; Reddy, N.S.; Yeom, J.-T.; Hong, J.-K.
Source: Self-asserted source
N. S. Reddy via Scopus - Elsevier
grade
Preferred source (of 2)‎

Modeling hot deformation behavior of low-cost Ti-2Al-9.2Mo-2Fe beta titanium alloy using a deep neural network

Journal of Materials Science and Technology
2019 | Journal article
Source: Self-asserted source
N. S. Reddy

Modeling hot deformation behavior of low-cost Ti-2Al-9.2Mo-2Fe beta titanium alloy using a deep neural network

Journal of Materials Science and Technology
2019 | Journal article
DOI: 10.1016/j.jmst.2018.11.018
EID:

2-s2.0-85060877974
Contributors: Li, C.-L.; Narayana, P.L.; Reddy, N.S.; Choi, S.-W.; Yeom, J.-T.; Hong, J.-K.; Park, C.H.
Source: Self-asserted source
N. S. Reddy via Scopus - Elsevier
grade
Preferred source (of 2)‎

Neural Network Approach to Construct a Processing Map from a Non-linear Stress?Temperature Relationship

Metals and Materials International
2019 | Journal article
Source: Self-asserted source
N. S. Reddy

Neural Network Approach to Construct a Processing Map from a Non-linear Stress–Temperature Relationship

Metals and Materials International
2019 | Journal article
DOI: 10.1007/s12540-018-00225-8
EID:

2-s2.0-85057625969
Contributors: Park, C.H.; Cha, D.; Kim, M.; Reddy, N.S.; Yeom, J.-T.
Source: Self-asserted source
N. S. Reddy via Scopus - Elsevier
grade
Preferred source (of 2)‎

Microstructural response of $̆pbeta$-stabilized Ti–6Al–4V manufactured by direct energy deposition

Journal of Alloys and Compounds
2019-11 | Journal article
DOI: 10.1016/j.jallcom.2019.152021
Source: Self-asserted source
N. S. Reddy
grade
Preferred source (of 4)‎

Effect of sodium salts on the cycling performance of tin anode in sodium ion batteries

Ionics
2018 | Journal article
Source: Self-asserted source
N. S. Reddy

Effect of sodium salts on the cycling performance of tin anode in sodium ion batteries

Ionics
2018 | Journal article
DOI: 10.1007/s11581-017-2243-2
EID:

2-s2.0-85028577003
Contributors: Sadan, M.K.; Choi, S.-H.; Kim, H.H.; Kim, C.; Cho, G.-B.; Kim, K.-W.; Reddy, N.S.; Ahn, J.-H.; Ahn, H.-J.
Source: Self-asserted source
N. S. Reddy via Scopus - Elsevier
grade
Preferred source (of 2)‎

Estimation of Transformation Temperatures in Ti?Ni?Pd Shape Memory Alloys

Metals and Materials International
2018 | Journal article
Source: Self-asserted source
N. S. Reddy

Estimation of Transformation Temperatures in Ti–Ni–Pd Shape Memory Alloys

Metals and Materials International
2018 | Journal article
DOI: 10.1007/s12540-018-0109-4
EID:

2-s2.0-85043689076
Contributors: Narayana, P.L.; Kim, S.-W.; Hong, J.-K.; Reddy, N.S.; Yeom, J.-T.
Source: Self-asserted source
N. S. Reddy via Scopus - Elsevier
grade
Preferred source (of 2)‎

Tensile properties of a newly developed high-temperature titanium alloy at room temperature and 650??C

Materials Science and Engineering A
2018 | Journal article
Source: Self-asserted source
N. S. Reddy

Tensile properties of a newly developed high-temperature titanium alloy at room temperature and 650 °C

Materials Science and Engineering A
2018 | Journal article
DOI: 10.1016/j.msea.2018.01.113
EID:

2-s2.0-85041481369
Contributors: Narayana, P.L.; Kim, S.-W.; Hong, J.-K.; Reddy, N.S.; Yeom, J.-T.
Source: Self-asserted source
N. S. Reddy via Scopus - Elsevier
grade
Preferred source (of 2)‎
Items per page:
Page 1 of 3

Peer review (14 reviews for 10 publications/grants)

Review activity for Ceramics international. (1)
Review activity for Computational materials science. (1)
Review activity for Environmental research. (1)
Review activity for Information fusion. (1)
Review activity for Journal of materials science. (1)
Review activity for Materials today communications. (4)
Review activity for Results in physics. (1)
Review activity for Science of the total environment. (1)
Review activity for Steel research international (1)
Review activity for Wear. (2)
How many people are using ORCID?