Suchandan K Das

951 total citations
66 papers, 667 citations indexed

About

Suchandan K Das is a scholar working on Mechanical Engineering, Materials Chemistry and Aerospace Engineering. According to data from OpenAlex, Suchandan K Das has authored 66 papers receiving a total of 667 indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Mechanical Engineering, 32 papers in Materials Chemistry and 16 papers in Aerospace Engineering. Recurrent topics in Suchandan K Das's work include Metallurgical Processes and Thermodynamics (14 papers), Microstructure and Mechanical Properties of Steels (11 papers) and Power Transformer Diagnostics and Insulation (11 papers). Suchandan K Das is often cited by papers focused on Metallurgical Processes and Thermodynamics (14 papers), Microstructure and Mechanical Properties of Steels (11 papers) and Power Transformer Diagnostics and Insulation (11 papers). Suchandan K Das collaborates with scholars based in India, Canada and Poland. Suchandan K Das's co-authors include Ashish Paramane, U. Mohan Rao, Sunil Kumar, Soumya Chatterjee, M. Ghosh, N.K. Mukhopadhyay, A K Pramanick, B. Ravi Kumar, D. K. Bhattacharya and B.C. Pai and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Journal of Physical Chemistry C and Physical Chemistry Chemical Physics.

In The Last Decade

Suchandan K Das

62 papers receiving 644 citations

Peers

Suchandan K Das

EEE

J.R. Laguna-Camacho Mexico

Tianjian Wang China

Shusen Cheng China

Jiaxiang Xue China

Masaki SHIRATORI Japan

G.A. Harmain India

A.R. Shahani Iran

Nagaraja Iyyer United States

Jun-Jiang Xiong China

J.R. Laguna-Camacho Mexico

Suchandan K Das

248 ×0.5

174 ×0.7

134 ×0.9

227 ×1.6

42 ×0.4

EEE

Citations per year, relative to Suchandan K Das Suchandan K Das (= 1×) peers J.R. Laguna-Camacho

Countries citing papers authored by Suchandan K Das

Since Specialization

Citations

This map shows the geographic impact of Suchandan K Das's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Suchandan K Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suchandan K Das more than expected).

Fields of papers citing papers by Suchandan K Das

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Suchandan K Das. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Suchandan K Das. The network helps show where Suchandan K Das may publish in the future.

Co-authorship network of co-authors of Suchandan K Das

This figure shows the co-authorship network connecting the top 25 collaborators of Suchandan K Das. A scholar is included among the top collaborators of Suchandan K Das based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Suchandan K Das. Suchandan K Das is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Pal, Snehanshu, et al.. (2025). Developing new high-entropy alloys with enhanced hardness using a hybrid machine learning approach: integrating interpretability and NSGA-II optimization. Journal of Materials Science. 60(10). 4820–4845. 2 indexed citations

Das, Suchandan K, et al.. (2024). Improved machine learning framework for prediction of phases and crystal structures of high entropy alloys. SHILAP Revista de lepidopterología. 9. 100144–100144. 3 indexed citations

Paramane, Ashish, et al.. (2024). Hybrid Algorithm for Dynamic Fault Prediction of HVDC Converter Transformer Using DGA Data. IEEE Transactions on Dielectrics and Electrical Insulation. 31(4). 2128–2135. 7 indexed citations

Das, Suchandan K, et al.. (2024). Recurrence Plot-Aided Deep Learning Framework for Condition Monitoring of Transformer Insulation Using Fourier Transform Infrared Spectroscopy. IEEE Transactions on Industry Applications. 60(6). 8393–8402.

Das, Suchandan K, Ashish Paramane, Soumya Chatterjee, & Mrutyunjay Maharana. (2024). Quantification of Aging State of Insulating Oils Using FTIR Spectra and Deep Learning. IEEE Transactions on Dielectrics and Electrical Insulation. 31(4). 1936–1943. 1 indexed citations

Das, Suchandan K, et al.. (2023). Sensing of Particulate Matter (PM 2.5 and PM 10) in the Air of Tier 1, Tier 2, and Tier 3 Cities in India Using EVDHM-ARIMA Hybrid Model. IEEE Sensors Letters. 7(5). 1–4. 7 indexed citations

Paramane, Ashish, et al.. (2023). Health Assessment of Solid Insulation of Mineral Oil-Filled Converter Transformer. IEEE Transactions on Dielectrics and Electrical Insulation. 31(3). 1557–1564. 3 indexed citations

Das, Suchandan K, Ashish Paramane, U. Mohan Rao, & Paweł Rózga. (2023). A Hybrid Regression Model to Estimate Remaining Useful Life of Transformer Liquid. IEEE Transactions on Dielectrics and Electrical Insulation. 31(2). 1062–1069. 6 indexed citations

Das, Suchandan K, Ashish Paramane, U. Mohan Rao, Soumya Chatterjee, & K. Sathish Kumar. (2023). Corrosive Dibenzyl Disulfide Concentration Prediction in Transformer Oil Using Deep Neural Network. IEEE Transactions on Dielectrics and Electrical Insulation. 30(4). 1608–1615. 15 indexed citations

10.

Das, Suchandan K, Ashish Paramane, Shweta Kumari, & Soumya Chatterjee. (2023). Deep Learning Aided Classification of Ageing Condition of Natural Ester Oils Using FTIR Analysis. 22. 1–6. 2 indexed citations

11.

Das, Suchandan K, et al.. (2023). Bi-directional Long Short Term Memory Based Classification of Sulphur Corrosion in Power Transformers Using Mineral Oil Data. 66–70.

12.

Das, Suchandan K, Ashish Paramane, Soumya Chatterjee, & U. Mohan Rao. (2022). Accurate Identification of Transformer Faults From Dissolved Gas Data Using Recursive Feature Elimination Method. IEEE Transactions on Dielectrics and Electrical Insulation. 30(1). 466–473. 44 indexed citations

13.

Choudhary, Om Prakash, et al.. (2021). IoT Enabled Condition Monitoring of Low Voltage Motors using Fuzzy Inference System. 132–137. 1 indexed citations

14.

Das, Suchandan K, et al.. (2020). Phenomenological kinetic model of the nano-bainitic steels to characterize the dynamics of the autocatalytic nucleation process. SN Applied Sciences. 2(4). 5 indexed citations

15.

Das, Suchandan K, et al.. (2015). Impact of Boiler Water Chemistry on Waterside Tube Failures. 2(4). 28–35. 7 indexed citations

16.

Das, Suchandan K, et al.. (2011). A mathematical model to characterise effects of liquid hold-up on bosh silicon transport in the dripping zone of a blast furnace. Applied Mathematical Modelling. 35(9). 4208–4221. 10 indexed citations

17.

Das, Suchandan K, et al.. (2007). A Mathematical Model for Prediction of Physical Properties of the Coke Oven Charge During Carbonisation. High Temperature Materials and Processes. 26(1). 43–58. 2 indexed citations

18.

Das, Suchandan K, et al.. (2006). A Mathematical Model to Characterize Volatile Matter Evolution during Carbonisation in Metallurgical Coke Ovens. 1 indexed citations

19.

Das, Suchandan K, et al.. (2006). Mathematical Modelling of Erosion Behaviour of Impacted Fly Ash Particles on Coal Fired Boiler Components at Elevated Temperature. High Temperature Materials and Processes. 25(5-6). 323–336. 8 indexed citations

20.

Das, Suchandan K. (1993). Numerical formalism for solving heat transfer in continuous casting process by nonorthogonal co-ordinate transformation strategy. Scandinavian Journal of Metallurgy. 22(4). 198–202. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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