Muhammad Umar Farooq

2.0k total citations
83 papers, 1.5k citations indexed

About

Muhammad Umar Farooq is a scholar working on Mechanical Engineering, Electrical and Electronic Engineering and Biomedical Engineering. According to data from OpenAlex, Muhammad Umar Farooq has authored 83 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Mechanical Engineering, 41 papers in Electrical and Electronic Engineering and 34 papers in Biomedical Engineering. Recurrent topics in Muhammad Umar Farooq's work include Advanced Machining and Optimization Techniques (41 papers), Advanced machining processes and optimization (41 papers) and Advanced Surface Polishing Techniques (29 papers). Muhammad Umar Farooq is often cited by papers focused on Advanced Machining and Optimization Techniques (41 papers), Advanced machining processes and optimization (41 papers) and Advanced Surface Polishing Techniques (29 papers). Muhammad Umar Farooq collaborates with scholars based in Pakistan, United Kingdom and Saudi Arabia. Muhammad Umar Farooq's co-authors include Saqib Anwar, Muhammad Asad Ali, Catalin I. Pruncu, Muhammad Sana, Amjad Hussain, Mohammad Pervez Mughal, Nadeem Ahmad Mufti, Muhammad Salman Habib, Tariq Masood and Rodolfo E. Haber and has published in prestigious journals such as Journal of Cleaner Production, Scientific Reports and Materials Science and Engineering A.

In The Last Decade

Muhammad Umar Farooq

79 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Muhammad Umar Farooq Pakistan 25 1.1k 773 551 158 141 83 1.5k
R. Jeyapaul India 18 918 0.9× 712 0.9× 468 0.8× 75 0.5× 93 0.7× 55 1.4k
Ibrahim Deiab Canada 28 1.8k 1.6× 1.0k 1.3× 690 1.3× 320 2.0× 245 1.7× 95 2.7k
Ming Chen China 26 1.8k 1.6× 878 1.1× 951 1.7× 83 0.5× 258 1.8× 128 2.2k
Kaushik Kumar India 21 808 0.8× 417 0.5× 392 0.7× 167 1.1× 245 1.7× 127 1.4k
Sarbjit Singh India 21 617 0.6× 446 0.6× 349 0.6× 58 0.4× 95 0.7× 95 1.3k
Jagadish Jagadish India 19 617 0.6× 412 0.5× 339 0.6× 81 0.5× 63 0.4× 66 1.0k
Kumar Abhishek India 24 1.3k 1.2× 766 1.0× 504 0.9× 158 1.0× 133 0.9× 104 1.7k
T. Senthilvelan India 23 1.1k 1.0× 524 0.7× 428 0.8× 87 0.6× 171 1.2× 65 1.5k
Barbara Linke United States 21 1.1k 1.0× 468 0.6× 657 1.2× 213 1.3× 106 0.8× 92 1.7k
Diego Carou Spain 21 1.4k 1.3× 651 0.8× 459 0.8× 337 2.1× 198 1.4× 52 1.7k

Countries citing papers authored by Muhammad Umar Farooq

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Umar Farooq

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Muhammad Umar Farooq. 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 Muhammad Umar Farooq. The network helps show where Muhammad Umar Farooq may publish in the future.

Co-authorship network of co-authors of Muhammad Umar Farooq

This figure shows the co-authorship network connecting the top 25 collaborators of Muhammad Umar Farooq. A scholar is included among the top collaborators of Muhammad Umar Farooq 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 Muhammad Umar Farooq. Muhammad Umar Farooq 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.
Ahmed, Nazir, Muhammad Umar Farooq, & Fuyi Chen. (2025). Materials discovery through reinforcement learning: a comprehensive review. 2 indexed citations
2.
Farooq, Muhammad Umar, et al.. (2025). High Throughput Low Latency Network Intrusion Detection on FPGAs: A Raw Packet Approach. 183–183. 1 indexed citations
3.
Farooq, Muhammad Umar, et al.. (2025). GDSSA-Net: A gradually deeply supervised self-ensemble attention network for IoMT-integrated thyroid nodule segmentation. Internet of Things. 31. 101598–101598. 2 indexed citations
4.
Farooq, Muhammad Umar, et al.. (2025). A novel approach to recoil pad enhancement in rifles via topological design with material extrusion and SLA. Frontiers in Mechanical Engineering. 11.
5.
Sana, Muhammad, et al.. (2024). Artificial neural networks-based modelling of effects of cryogenic electrode treatment, nano-powder, and surfactant-mixed dielectrics on wear performance and dimensional errors on superalloy machining. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 46(9). 12 indexed citations
6.
Mehmood, Shahid, Muhammad Asad Ali, Muhammad Huzaifa Raza, et al.. (2024). Machining performance, economic and environmental analyses and multi-criteria optimization of electric discharge machining for SS310 alloy. Scientific Reports. 14(1). 28930–28930. 7 indexed citations
7.
Farooq, Muhammad Umar, et al.. (2023). Mask-Transformer-Based Networks for Teeth Segmentation in Panoramic Radiographs. Bioengineering. 10(7). 843–843. 16 indexed citations
8.
Farooq, Muhammad Umar, et al.. (2023). Reducing micro-machining errors during electric discharge machining of titanium alloy using nonionic liquids. Materials and Manufacturing Processes. 39(4). 449–464. 26 indexed citations
9.
Farooq, Muhammad Umar, et al.. (2023). Sustainable machining of additive manufactured SS-316L underpinning low carbon manufacturing goal. Journal of Materials Research and Technology. 24. 2299–2318. 29 indexed citations
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Sana, Muhammad, Muhammad Umar Farooq, Saqib Anwar, & Rodolfo E. Haber. (2023). Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining. Heliyon. 9(12). e22508–e22508. 26 indexed citations
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Khan, Sarmad Ali, et al.. (2023). Investigation on tool wear mechanisms and machining tribology of hardened DC53 steel through modified CBN tooling geometry in hard turning. The International Journal of Advanced Manufacturing Technology. 127(1-2). 547–564. 7 indexed citations
16.
Farooq, Muhammad Umar, Saqib Anwar, M. Saravana Kumar, et al.. (2022). A Novel Flushing Mechanism to Minimize Roughness and Dimensional Errors during Wire Electric Discharge Machining of Complex Profiles on Inconel 718. Materials. 15(20). 7330–7330. 29 indexed citations
17.
Khan, Sarmad Ali, Mudassar Rehman, Muhammad Umar Farooq, et al.. (2021). A Detailed Machinability Assessment of DC53 Steel for Die and Mold Industry through Wire Electric Discharge Machining. Metals. 11(5). 816–816. 29 indexed citations
18.
Ishfaq, Kashif, Muhammad Umar Farooq, Saqib Anwar, et al.. (2020). A comprehensive investigation of geometrical accuracy errors during WEDM of Al6061-7.5%SiC composite. Materials and Manufacturing Processes. 36(3). 362–372. 37 indexed citations
19.
Ali, Ashaq, Jieqiong Zhou, Muhammad Yousaf, et al.. (2020). Evaluating antibody response pattern in asymptomatic virus infected pregnant females: Human well-being study. Journal of King Saud University - Science. 33(1). 101255–101255. 4 indexed citations
20.
Mughal, Mohammad Pervez, Muhammad Umar Farooq, Jabir Mumtaz, et al.. (2020). Surface modification for osseointegration of Ti6Al4V ELI using powder mixed sinking EDM. Journal of the mechanical behavior of biomedical materials. 113. 104145–104145. 66 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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