Vijaykumar S. Jatti

1.3k total citations
78 papers, 892 citations indexed

About

Vijaykumar S. Jatti is a scholar working on Mechanical Engineering, Electrical and Electronic Engineering and Automotive Engineering. According to data from OpenAlex, Vijaykumar S. Jatti has authored 78 papers receiving a total of 892 indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Mechanical Engineering, 25 papers in Electrical and Electronic Engineering and 23 papers in Automotive Engineering. Recurrent topics in Vijaykumar S. Jatti's work include Advanced Machining and Optimization Techniques (24 papers), Advanced machining processes and optimization (23 papers) and Additive Manufacturing and 3D Printing Technologies (23 papers). Vijaykumar S. Jatti is often cited by papers focused on Advanced Machining and Optimization Techniques (24 papers), Advanced machining processes and optimization (23 papers) and Additive Manufacturing and 3D Printing Technologies (23 papers). Vijaykumar S. Jatti collaborates with scholars based in India, Italy and Taiwan. Vijaykumar S. Jatti's co-authors include T.P. Singh, Nitin Khedkar, Akshansh Mishra, S. Prakash, A. Krishnamoorthy, Eyob Messele Sefene, Nguyen Huu Phan, Ravi Sekhar, Meena Laad and Emad Abouel Nasr and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and The International Journal of Advanced Manufacturing Technology.

In The Last Decade

Vijaykumar S. Jatti

73 papers receiving 838 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vijaykumar S. Jatti India 15 599 283 278 268 178 78 892
Sathish Kannan United Arab Emirates 17 1.0k 1.7× 421 1.5× 503 1.8× 185 0.7× 136 0.8× 132 1.3k
Ranganath M. Singari India 18 579 1.0× 179 0.6× 154 0.6× 162 0.6× 95 0.5× 61 817
Atul Babbar India 19 540 0.9× 463 1.6× 201 0.7× 197 0.7× 69 0.4× 68 913
Stefano Guarino Italy 18 583 1.0× 207 0.7× 112 0.4× 255 1.0× 91 0.5× 64 923
Abdul Wahab Hashmi India 17 517 0.9× 257 0.9× 133 0.5× 222 0.8× 91 0.5× 46 873
Sharanjit Singh India 12 612 1.0× 302 1.1× 326 1.2× 285 1.1× 155 0.9× 23 806
Shashikant Joshi India 14 418 0.7× 186 0.7× 180 0.6× 115 0.4× 82 0.5× 33 535
Michela Simoncini Italy 24 1.5k 2.5× 246 0.9× 204 0.7× 210 0.8× 132 0.7× 121 1.8k
Ana Vafadar Australia 16 971 1.6× 308 1.1× 323 1.2× 398 1.5× 174 1.0× 34 1.3k

Countries citing papers authored by Vijaykumar S. Jatti

Since Specialization
Citations

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

Fields of papers citing papers by Vijaykumar S. Jatti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vijaykumar S. Jatti. 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 Vijaykumar S. Jatti. The network helps show where Vijaykumar S. Jatti may publish in the future.

Co-authorship network of co-authors of Vijaykumar S. Jatti

This figure shows the co-authorship network connecting the top 25 collaborators of Vijaykumar S. Jatti. A scholar is included among the top collaborators of Vijaykumar S. Jatti 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 Vijaykumar S. Jatti. Vijaykumar S. Jatti 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.
Mishra, Akshansh & Vijaykumar S. Jatti. (2024). A cutting-edge framework for surface roughness prediction using multiverse optimization-driven machine learning algorithms. International Journal on Interactive Design and Manufacturing (IJIDeM). 18(7). 5243–5260. 1 indexed citations
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Mishra, Akshansh, Vijaykumar S. Jatti, Eyob Messele Sefene, & Emad Makki. (2024). Homogenization of Inconel 625 based periodic auxetic lattice structures with varying strut thickness. APL Materials. 12(2).
4.
Mishra, Akshansh, et al.. (2024). Novel neurosymbolic artificial intelligence (NSAI) based algorithm to predict specific energy absorption in CoCrMo based architected materials. International Journal of Information Technology. 17(2). 1111–1118. 2 indexed citations
5.
Mishra, Akshansh, Vijaykumar S. Jatti, & Eyob Messele Sefene. (2024). Assessing the Process-Property Relationship in Laser Powder Bed Fusion of AlSi10Mg Using Kalman Filter-Based Machine Learning Algorithms. Journal of Materials Engineering and Performance. 34(14). 14920–14937. 2 indexed citations
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Krishnamoorthy, A., Vijaykumar S. Jatti, Nitin Ambhore, et al.. (2023). Fatigue analysis of electro discharge machined Nitinol 60. 10. 2 indexed citations
9.
Sefene, Eyob Messele, et al.. (2023). A multi-criterion optimization of mechanical properties and sustainability performance in friction stir welding of 6061-T6 AA. Materials Today Communications. 36. 106838–106838. 17 indexed citations
10.
Mishra, Akshansh & Vijaykumar S. Jatti. (2023). Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm. Lubricants. 11(6). 261–261. 5 indexed citations
11.
Mishra, Akshansh, et al.. (2023). Fracture analysis of friction stir spot welded acrylonitrile butadiene styrene sheet in butt configuration. Materials Research Express. 10(5). 55302–55302. 1 indexed citations
12.
Mishra, Akshansh & Vijaykumar S. Jatti. (2023). Novel Coupled Genetic Algorithm–Machine Learning Approach for Predicting Surface Roughness in Fused Deposition Modeling of Polylactic Acid Specimens. Journal of Materials Engineering and Performance. 33(12). 6136–6145. 10 indexed citations
13.
Mishra, Akshansh & Vijaykumar S. Jatti. (2023). Neurosymbolic artificial intelligence (NSAI) based algorithm for predicting the impact strength of additive manufactured polylactic acid (PLA) specimens. Engineering Research Express. 5(3). 35017–35017. 8 indexed citations
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Mishra, Akshansh & Vijaykumar S. Jatti. (2023). Reinforcement learning based approach for the optimization of mechanical properties of additively manufactured specimens. International Journal on Interactive Design and Manufacturing (IJIDeM). 17(4). 2045–2053. 9 indexed citations
16.
Mishra, Akshansh, et al.. (2023). Computer Vision Algorithm for Predicting the Welding Efficiency of Friction Stir Welded Copper Joints from its Microstructures. SHILAP Revista de lepidopterología. 2 indexed citations
17.
Dey, Tapobrata, et al.. (2022). Aluminum alloy and galvanized steel CMT weld joints for lightweight automobile applications. Engineering Research Express. 4(3). 35001–35001. 2 indexed citations
18.
Krishnamoorthy, A., et al.. (2019). Process Parameters Optimization Using Jaya Algorithm During Edm Machining Of Niti60 Alloy. International journal of scientific and technology research. 8(11). 1168–1174. 1 indexed citations
19.
Jatti, Vijaykumar S., et al.. (2019). A Study On Effect Of Fused Deposition Modeling Process Parameters On Mechanical Properties. International journal of scientific and technology research. 8(11). 689–693. 23 indexed citations
20.
Jatti, Vijaykumar S., et al.. (2015). Electric discharge machining of cryo-treated NiTi alloy by cryo-treated and untreated copper electrode. Journal of chemical and pharmaceutical research. 7(7). 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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