Farrokh Sassani

3.0k citations
96 papers · 2.3k · 1 hit paper · h-index 29

Impact in

Papers in

Farrokh Sassani

93 papers receiving 2.2k citations

Farrokh Sassani's Hit Papers

A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges 2021 · 206 citations
2060+1+3Years since publication50100150200

Peers

Farrokh Sassani
Comparison fields: 5 of 129
  • Industrial and Manufacturing Engineering 528
  • Control and Systems Engineering 709
  • Software 116
  • Mechanical Engineering 982
  • Safety, Risk, Reliability and Quality 189
Replace Alaa Elwany with:
Alaa Elwany United States
Limin Jia China
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Andrew Starr United Kingdom
Tiedo Tinga Netherlands
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Andy Tan Australia
Hongfu Zuo China
Saïd Rechak Algeria
Cheng‐Geng Huang China
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Citations per field
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Citations per year

Countries citing papers authored by Farrokh Sassani

Since Specialization
Citations

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

Fields of papers citing papers by Farrokh Sassani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Farrokh Sassani, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Farrokh Sassani Line = papers co-authored together Farrokh Sassani links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 96 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2007234
2
A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges
Hit paper breakdown →
2021206
3 199886
4 201080
5 200979
6 200978
7 200175
8 200271
9 200767
10 201364
11 199956
12 200755
13 199951
14 201950
15 201749
16 201149
17 200247
18 202247
19 201043
20 201942

About Farrokh Sassani

Farrokh Sassani is a scholar working on Mechanical Engineering, Control and Systems Engineering, Industrial and Manufacturing Engineering, Electrical and Electronic Engineering and Mechanics of Materials, having authored 96 papers that have together received 2.3k indexed citations. Recurring topics across this work include Fault Detection and Control Systems (11 papers), Advanced Manufacturing and Logistics Optimization (10 papers), Manufacturing Process and Optimization (10 papers), Robotic Mechanisms and Dynamics (9 papers), Wood Treatment and Properties (9 papers), Hydraulic and Pneumatic Systems (8 papers), Optimization and Packing Problems (7 papers) and Robot Manipulation and Learning (7 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (528 citations), Control and Systems Engineering (709 citations), Software (116 citations), Mechanical Engineering (982 citations) and Safety, Risk, Reliability and Quality (189 citations). Farrokh Sassani has collaborated with scholars based in Canada, Iran and United States. Frequent co-authors include Vahid Nasir, Reza Tavakkoli‐Moghaddam, Septimiu E. Salcudean, Leo Stocco, Jalal Safari, Boris Stoeber, Farid Ullah Khan, Nima Safaei, P.D. Lawrence and Julie Cool. Their work appears in journals such as Robotica, The International Journal of Advanced Manufacturing Technology, IEEE/ASME Transactions on Mechatronics, SAE technical papers on CD-ROM/SAE technical paper series and Journal of Dynamic Systems Measurement and Control.

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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