K. Rameshkumar

1.3k total citations
62 papers, 849 citations indexed

About

K. Rameshkumar is a scholar working on Mechanical Engineering, Industrial and Manufacturing Engineering and Artificial Intelligence. According to data from OpenAlex, K. Rameshkumar has authored 62 papers receiving a total of 849 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Mechanical Engineering, 19 papers in Industrial and Manufacturing Engineering and 11 papers in Artificial Intelligence. Recurrent topics in K. Rameshkumar's work include Advanced machining processes and optimization (19 papers), Welding Techniques and Residual Stresses (12 papers) and Scheduling and Optimization Algorithms (11 papers). K. Rameshkumar is often cited by papers focused on Advanced machining processes and optimization (19 papers), Welding Techniques and Residual Stresses (12 papers) and Scheduling and Optimization Algorithms (11 papers). K. Rameshkumar collaborates with scholars based in India, United Arab Emirates and Australia. K. Rameshkumar's co-authors include A. Sumesh, P. Krishnakumar, K.I. Ramachandran, S.P. Anbuudayasankar, Jason C.H. Chen, B. Latha Shankar, Chandrasekharan Rajendran, Avinash Ravi Raja, Binoy B. Nair and Dinu Thomas Thekkuden and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

In The Last Decade

K. Rameshkumar

58 papers receiving 800 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
K. Rameshkumar India 17 527 225 187 109 86 62 849
Hakkı Özgür Ünver Türkiye 18 675 1.3× 411 1.8× 428 2.3× 162 1.5× 108 1.3× 43 1.1k
Fernando Romero Spain 12 392 0.7× 408 1.8× 146 0.8× 81 0.7× 65 0.8× 46 677
Connor Jennings United States 6 360 0.7× 265 1.2× 191 1.0× 69 0.6× 129 1.5× 10 684
Saman Khalilpourazary Iran 14 245 0.5× 112 0.5× 135 0.7× 72 0.7× 111 1.3× 29 758
Farhad Kolahan Iran 19 714 1.4× 257 1.1× 271 1.4× 152 1.4× 36 0.4× 80 1.1k
Sasan Sattarpanah Karganroudi Canada 15 301 0.6× 202 0.9× 73 0.4× 38 0.3× 118 1.4× 52 712
Srinivas Soumitri Miriyala India 15 362 0.7× 60 0.3× 275 1.5× 167 1.5× 118 1.4× 33 717
Tatjana Šibalija Serbia 15 305 0.6× 166 0.7× 156 0.8× 89 0.8× 34 0.4× 28 586
Gerardo Beruvides Spain 18 253 0.5× 261 1.2× 162 0.9× 87 0.8× 121 1.4× 30 704
Zsolt János Viharos Hungary 15 253 0.5× 219 1.0× 99 0.5× 65 0.6× 150 1.7× 87 705

Countries citing papers authored by K. Rameshkumar

Since Specialization
Citations

This map shows the geographic impact of K. Rameshkumar'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 K. Rameshkumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites K. Rameshkumar more than expected).

Fields of papers citing papers by K. Rameshkumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by K. Rameshkumar. 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 K. Rameshkumar. The network helps show where K. Rameshkumar may publish in the future.

Co-authorship network of co-authors of K. Rameshkumar

This figure shows the co-authorship network connecting the top 25 collaborators of K. Rameshkumar. A scholar is included among the top collaborators of K. Rameshkumar 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 K. Rameshkumar. K. Rameshkumar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Rameshkumar, K., et al.. (2025). Roughness prediction in end milling of Ti1023 alloy using machine learning-based Gaussian Process Regression. Procedia Computer Science. 253. 455–464.
2.
3.
Rameshkumar, K., et al.. (2024). Simulation analysis and development of priority dispatching rules for a partial flexible job shop. International Journal of Services and Operations Management. 48(4). 576–601.
4.
Rameshkumar, K., et al.. (2024). Chatter Identification in Milling of Titanium Alloy Using Machine Learning Approaches with Non-Linear Features of Cutting Force and Vibration Signatures. International Journal of Prognostics and Health Management. 15(1). 9 indexed citations
5.
Sumesh, A., et al.. (2024). Enhancing the productivity of mould shop using continuous improvement tools and simulation. International Journal of Systems Science Operations & Logistics. 11(1). 1 indexed citations
6.
Rameshkumar, K., et al.. (2024). Mapping of Strategic Operating Conditions for End Milling Super-Transus Heat-Treated Ti1023 Alloy Using Multi-Objective Optimization. Arabian Journal for Science and Engineering. 50(12). 8995–9023. 1 indexed citations
7.
Alagarsamy, Manjunathan, et al.. (2024). Gesture Controlled Vehicle for Armed Service. 1–6. 1 indexed citations
8.
Rameshkumar, K., et al.. (2023). Multi-criteria scheduling of realistic flexible job shop: a novel approach for integrating simulation modelling and multi-criteria decision making. International Journal of Production Research. 62(1-2). 336–358. 11 indexed citations
9.
Rakesh, Nitin, et al.. (2022). Effect of fluxes on weld penetration during TIG welding – A review. Materials Today Proceedings. 72. 3040–3048. 8 indexed citations
10.
Rameshkumar, K., et al.. (2021). Machine Learning Models for Predicting Grinding Wheel Conditions Using Acoustic Emission Features. SAE International Journal of Materials and Manufacturing. 14(4). 387–406. 3 indexed citations
11.
Rameshkumar, K., et al.. (2021). Development and analysis of priority decision rules using MCDM approach for a flexible job shop scheduling: A simulation study. Simulation Modelling Practice and Theory. 114. 102416–102416. 37 indexed citations
12.
Rameshkumar, K., et al.. (2019). Examine and Visualising Packet Capture Files. International Journal of Recent Technology and Engineering (IJRTE). 8(2S11). 3930–3933. 1 indexed citations
13.
Anbuudayasankar, S.P., et al.. (2018). Optimizing bi-objective, multi-echelon supply chain model using particle swarm intelligence algorithm. IOP Conference Series Materials Science and Engineering. 310. 12025–12025. 1 indexed citations
14.
Rameshkumar, K., et al.. (2017). Construction of a Low Cost Cutting Tool Dynamometer and Static Calibration of Measuring Cutting Force in a CNC Milling Machine. SAE technical papers on CD-ROM/SAE technical paper series. 1. 1 indexed citations
15.
Sumesh, A., et al.. (2015). Acoustic Signature Based Weld Quality Monitoring for SMAW Process Using Data Mining Algorithms. Applied Mechanics and Materials. 813-814. 1104–1113. 18 indexed citations
16.
Rameshkumar, K., et al.. (2015). Performance Analysis of Classification Algorithms on Medical Diagnoses-a Survey. Journal of Computer Science. 11(1). 30–52. 11 indexed citations
17.
Rameshkumar, K., et al.. (2014). A Complete Survey on application of Frequent Pattern Mining and Association Rule Mining on Crime Pattern Mining. 19 indexed citations
18.
Rameshkumar, K.. (2011). A NOVEL ALGORITHM FOR ASSOCIATION RULE MINING FROM DATA WITH INCOMPLETE AND MISSING VALUES. SHILAP Revista de lepidopterología. 1(4). 171–177. 2 indexed citations
19.
Arumugam, S., et al.. (2009). A novel discrete particle swarm clustering algorithm for data clustering. 1–4. 2 indexed citations
20.
Rameshkumar, K., et al.. (2008). INFORMATION QUALITY IMPROVEMENT THROUGH ASSOCIATION RULE MINING ALGORITHMS DFCI, DFAPRIORI-CLOSE, EARA, PBAARA, SBAARA.. 2 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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