Prahar M. Bhatt
- Industrial and Manufacturing Engineering top 1%
- Automotive Engineering top 2%
- Mechanical Engineering top 10%
- Control and Systems Engineering top 5%
- Computer Vision and Pattern Recognition top 5%
- Co-authors
- Satyandra K. GuptaRishi K. MalhanYeo Jung YoonP. RajendranBrual C. ShahShantanu ThakarAriyan M. KabirAniruddha V. Shembekar
- Topics
- Robot Manipulation and Learning (17 papers)Additive Manufacturing and 3D Printing Technologies (15 papers)Manufacturing Process and Optimization (10 papers)
- Journals
- Additive manufacturingThe International Journal of Advanced Manufacturing TechnologyRobotics and Autonomous Systems
- Partner nations
- United States
In The Last Decade
Prahar M. Bhatt
31 papers receiving 768 citations
Hit Papers
Peers
Comparison fields: 5 of 69
- Industrial and Manufacturing Engineering 335
- Automotive Engineering 321
- Mechanical Engineering 289
- Control and Systems Engineering 212
- Computer Vision and Pattern Recognition 179
Countries citing papers authored by Prahar M. Bhatt
This map shows the geographic impact of Prahar M. Bhatt'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 Prahar M. Bhatt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Prahar M. Bhatt more than expected).
Fields of papers citing papers by Prahar M. Bhatt
This network shows the impact of papers produced by Prahar M. Bhatt. 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 Prahar M. Bhatt. The network helps show where Prahar M. Bhatt may publish in the future.
Co-authorship network of co-authors of Prahar M. Bhatt
This figure shows the co-authorship network connecting the top 25 collaborators of Prahar M. Bhatt. A scholar is included among the top collaborators of Prahar M. Bhatt 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 Prahar M. Bhatt. Prahar M. Bhatt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 11 | |
| 3 | 5 | |
| 4 | 1 | |
| 5 | Image-Based Surface Defect Detection Using Deep Learning: A Reviewbreakdown → | 205 |
| 6 | 9 | |
| 7 | 17 | |
| 8 | 3 | |
| 9 | 8 | |
| 10 | 6 | |
| 11 | 6 | |
| 12 | 13 | |
| 13 | 18 | |
| 14 | 49 | |
| 15 | 40 | |
| 16 | 7 | |
| 17 | 13 | |
| 18 | 16 | |
| 19 | 17 | |
| 20 | 22 |
About Prahar M. Bhatt
Prahar M. Bhatt is a scholar working on Automotive Engineering, Industrial and Manufacturing Engineering and Control and Systems Engineering, having authored 31 papers that have together received 783 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (17 papers), Additive Manufacturing and 3D Printing Technologies (15 papers) and Manufacturing Process and Optimization (10 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (335 citations), Automotive Engineering (321 citations) and Building and Construction (145 citations). Prahar M. Bhatt has collaborated with scholars based in United States. Frequent co-authors include Satyandra K. Gupta, Rishi K. Malhan, Yeo Jung Yoon, P. Rajendran, Brual C. Shah, Shantanu Thakar, Ariyan M. Kabir, Aniruddha V. Shembekar, Hugh A. Bruck and Ashish Kulkarni. Their work appears in journals such as Additive manufacturing, The International Journal of Advanced Manufacturing Technology and Robotics and Autonomous Systems.
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.