M. Amrita

707 total citations
26 papers, 589 citations indexed

About

M. Amrita is a scholar working on Mechanical Engineering, Mechanics of Materials and Electrical and Electronic Engineering. According to data from OpenAlex, M. Amrita has authored 26 papers receiving a total of 589 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Mechanical Engineering, 13 papers in Mechanics of Materials and 10 papers in Electrical and Electronic Engineering. Recurrent topics in M. Amrita's work include Advanced machining processes and optimization (18 papers), Tribology and Wear Analysis (12 papers) and Advanced Machining and Optimization Techniques (10 papers). M. Amrita is often cited by papers focused on Advanced machining processes and optimization (18 papers), Tribology and Wear Analysis (12 papers) and Advanced Machining and Optimization Techniques (10 papers). M. Amrita collaborates with scholars based in India and United States. M. Amrita's co-authors include R. R. Srikant, Rukmini Srikant Revuru, P. Vamsi Krishna, A. Venu Gopal, Bhanu Kiran Goriparthi, N. Mohan Rao, K.S. Reddy, V. Srinivas, Nilima Roy and K.V. Sathish and has published in prestigious journals such as The International Journal of Advanced Manufacturing Technology, Materials and Manufacturing Processes and Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture.

In The Last Decade

M. Amrita

24 papers receiving 566 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Amrita India 11 542 307 174 144 105 26 589
R. R. Srikant India 13 647 1.2× 366 1.2× 225 1.3× 154 1.1× 115 1.1× 27 706
Sarmad Ali Khan Pakistan 16 516 1.0× 339 1.1× 251 1.4× 104 0.7× 79 0.8× 37 562
Balwinder Singh Sidhu India 7 384 0.7× 216 0.7× 140 0.8× 107 0.7× 125 1.2× 17 505
Alper Uysal Türkiye 15 690 1.3× 439 1.4× 278 1.6× 104 0.7× 94 0.9× 72 754
Ravi Shankar Anand India 11 429 0.8× 247 0.8× 236 1.4× 69 0.5× 66 0.6× 27 483
M. Dhananchezian India 10 443 0.8× 206 0.7× 150 0.9× 53 0.4× 205 2.0× 25 474
Erhan Altan Türkiye 11 427 0.8× 277 0.9× 225 1.3× 65 0.5× 63 0.6× 34 453
Marcelo Araújo Câmara Brazil 10 337 0.6× 191 0.6× 181 1.0× 109 0.8× 117 1.1× 36 446
Ergün Ekı̇cı̇ Türkiye 15 635 1.2× 328 1.1× 224 1.3× 75 0.5× 224 2.1× 31 688
Tahsin Tecelli Öpöz United Kingdom 11 419 0.8× 264 0.9× 341 2.0× 76 0.5× 83 0.8× 31 492

Countries citing papers authored by M. Amrita

Since Specialization
Citations

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

Fields of papers citing papers by M. Amrita

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Amrita

This figure shows the co-authorship network connecting the top 25 collaborators of M. Amrita. A scholar is included among the top collaborators of M. Amrita 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 M. Amrita. M. Amrita 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.
2.
Amrita, M., et al.. (2025). Vibration Analysis of Graphene Reinforced Hybrid Jute/Basalt Natural Nanocomposite Beam. Journal of Vibration Engineering & Technologies. 13(7).
3.
Amrita, M., et al.. (2022). Multi-response optimization in machining Ti6Al4V using graphene dispersed emulsifier oil. Materials Today Proceedings. 62. 1179–1188. 1 indexed citations
4.
Amrita, M., et al.. (2022). Mechanical characterization of jute-basalt hybrid composites with graphene as nanofiller. Journal of Mechanical Science and Technology. 36(8). 3923–3929. 9 indexed citations
5.
Amrita, M., et al.. (2021). Tribological Behavior of Graphene-Dispersed Emulsifier Cutting Fluid. Journal of The Institution of Engineers (India) Series C. 4 indexed citations
6.
Amrita, M. & R. R. Srikant. (2021). Quantitative analysis of dispersion, cooling and lubricating properties of graphene dispersed emulsifier oil. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 43(2). 5 indexed citations
7.
8.
Amrita, M., et al.. (2021). Green machining using graphene-based self-lubricating cutting tool – a preliminary investigation. World Journal of Engineering. 19(6). 745–754. 1 indexed citations
9.
Amrita, M., et al.. (2021). The Investigation of Machinability and Surface Properties of Aluminium Alloy Matrix Composites. Journal of Engineering and Technological Sciences. 53(4). 210412–210412. 6 indexed citations
10.
Amrita, M., et al.. (2021). Analysis of hybrid nanofluids in machining AISI 4340 using minimum quantity lubrication. Materials Today Proceedings. 43. 579–586. 19 indexed citations
11.
Amrita, M., et al.. (2020). Optimisation of cutting parameters for cutting temperature and tool wear in turning AISI4140 under different cooling conditions. Advances in Materials and Processing Technologies. 8(sup1). 240–258. 15 indexed citations
12.
Amrita, M., et al.. (2019). Thermal enhancement of graphene dispersed emulsifier cutting fluid with different surfactants. Materials Research Express. 6(12). 125030–125030. 8 indexed citations
13.
Revuru, Rukmini Srikant, et al.. (2018). Minimum Quantity Lubrication Using Nano-Graphite Cutting Fluids for Sustainable Machining of AISI 4140 under Different Cutting Conditions. UNI ScholarWorks (University of Northern Iowa). 2(1). 114–136. 1 indexed citations
14.
Gopal, A. Venu, et al.. (2017). Experimental investigation of graphene nanoplatelets–based minimum quantity lubrication in grinding Inconel 718. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 233(2). 400–410. 51 indexed citations
15.
Revuru, Rukmini Srikant, et al.. (2017). Application of cutting fluids in machining of titanium alloys—a review. The International Journal of Advanced Manufacturing Technology. 91(5-8). 2477–2498. 111 indexed citations
16.
Amrita, M., et al.. (2015). Performance Evaluation And Economic Analysis Of Minimum Quantity Lubrication With Pressurized/Non-Pressurized Air And Nanofluid Mixture. Zenodo (CERN European Organization for Nuclear Research). 9(6). 1012–1017. 4 indexed citations
17.
Amrita, M., et al.. (2014). Experimental investigation on application of emulsifier oil based nano cutting fluids in metal cutting process. Procedia Engineering. 97. 115–124. 46 indexed citations
18.
Amrita, M., et al.. (2013). Nanofluids as a potential solution for Minimum Quantity Lubrication: A review. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 228(1). 3–20. 66 indexed citations
19.
Amrita, M., et al.. (2013). Preparation and characterization of properties of nanographite-based cutting fluid for machining operations. Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology. 228(3). 243–252. 30 indexed citations
20.
Amrita, M., et al.. (2013). Evaluation of Cutting Fluid With Nanoinclusions. Journal of Nanotechnology in Engineering and Medicine. 4(3). 12 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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