Derek G. Ford

48 papers receiving 263 citations

Peers

Derek G. Ford
Comparison fields: 5 of 27
  • Mechanical Engineering 291
  • Industrial and Manufacturing Engineering 57
  • Statistics, Probability and Uncertainty 32
  • Control and Systems Engineering 100
  • Computational Mechanics 71
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Timo Würz Germany
Yukitoshi IHARA Japan
Eneko Gomez-Acedo Spain
Dun Lu China
S.H.H. Zargarbashi Canada
Qun-Bao Xiao China
Vladimir T. Portman Israel
Zhouxiang Jiang China
Jianghai Shi China
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Citations per year

Countries citing papers authored by Derek G. Ford

Since Specialization
Citations

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

Fields of papers citing papers by Derek G. Ford

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Derek G. Ford. 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 Derek G. Ford. The network helps show where Derek G. Ford may publish in the future.

Co-authors

The 17 scholars most cited alongside Derek G. Ford, 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 Derek G. Ford Line = papers co-authored together Derek G. Ford links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 51 papers — load more, or switch the sort, to bring in the rest.

#Work
1 199938
2 199827
3
Laser Metrology and Machine Performance VI
200325
4 200422
5 200420
6 199719
7 200314
8 200011
9 19709
10 20039
11 19999
12 19708
13 20048
14 20037
15 19706
16 19706
17 19706
18 20036
19 20015
20 20035

About Derek G. Ford

Derek G. Ford is a scholar working on Mechanical Engineering, Industrial and Manufacturing Engineering, Control and Systems Engineering, Biomedical Engineering and Civil and Structural Engineering, having authored 51 papers that have together received 323 indexed citations. Recurring topics across this work include Advanced machining processes and optimization (38 papers), Advanced Measurement and Metrology Techniques (31 papers), Manufacturing Process and Optimization (16 papers), Iterative Learning Control Systems (14 papers), Advanced Surface Polishing Techniques (8 papers), Engineering Technology and Methodologies (6 papers), Advanced Sensor Technologies Research (4 papers) and Vibration and Dynamic Analysis (3 papers). The work is most often cited by research in Mechanical Engineering (291 citations), Industrial and Manufacturing Engineering (57 citations), Statistics, Probability and Uncertainty (32 citations), Control and Systems Engineering (100 citations) and Computational Mechanics (71 citations). Derek G. Ford has collaborated with scholars based in United Kingdom. Frequent co-authors include Crinela Pıslaru, Jim Freeman, Andrew P. Longstaff, Simon Fletcher, Andrew White, D. Morton, Alan Myers, Kai Cheng, Simon Barrans and Qiang Xu. Their work appears in journals such as Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, International Journal of Machine Tools and Manufacture, Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, The International Journal of Advanced Manufacturing Technology and Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering.

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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