Jay Young

571 citations
29 papers · 448 · h-index 12

Impact in

Papers in

    • Microstructure and Mechanical Properties of Steels 6
    • Aluminum Alloys Composites Properties 4
    • Intermetallics and Advanced Alloy Properties 3
    • Metal Alloys Wear and Properties 8

Jay Young

27 papers receiving 423 citations

Peers

Jay Young
Comparison fields: 5 of 78
  • Orthodontics 67
  • Oral Surgery 37
  • Computer Vision and Pattern Recognition 113
  • Mechanical Engineering 136
  • Artificial Intelligence 112
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Citations per field
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Citations per year

Countries citing papers authored by Jay Young

Since Specialization
Citations

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

Fields of papers citing papers by Jay Young

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jay Young, 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 Jay Young Line = papers co-authored together Jay Young links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017141
2
Fracture resistance of Kevlar-reinforced poly(methyl methacrylate) resin: a preliminary study.
199187
3 199424
4 201715
5 199015
6 199515
7 201214
8 201813
9 201213
10 198813
11 199313
12 201713
13 201512
14 199210
15 19878
16
Predicting Situated Behaviour Using Sequences of Abstract Spatial Relations.
20137
17 19986
18 19885
19 19935
20 20144

About Jay Young

Jay Young is a scholar working on Mechanical Engineering, Materials Chemistry, Computer Vision and Pattern Recognition, Mechanics of Materials and Aerospace Engineering, having authored 29 papers that have together received 448 indexed citations. Recurring topics across this work include Metal Alloys Wear and Properties (8 papers), Microstructure and Mechanical Properties of Steels (6 papers), Aluminum Alloy Microstructure Properties (4 papers), Aluminum Alloys Composites Properties (4 papers), Reinforcement Learning in Robotics (4 papers), Metallurgy and Material Forming (3 papers), AI-based Problem Solving and Planning (3 papers) and Intermetallics and Advanced Alloy Properties (3 papers). The work is most often cited by research in Orthodontics (67 citations), Oral Surgery (37 citations), Computer Vision and Pattern Recognition (113 citations), Mechanical Engineering (136 citations) and Artificial Intelligence (112 citations). Jay Young has collaborated with scholars based in United Kingdom, Austria and Italy. Frequent co-authors include Nick Hawes, Maurice Grech, Raymond Edward Smallman, Leposava Šiđanin, Seyed mohammad ali Boutorabi, Lars Kunze, Barbara Caputo, Valerio Basile, Elena Cabrio and Fran Smith. Their work appears in journals such as Materials Science and Technology, IEEE Robotics & Automation Magazine, Wear, Journal of Microscopy and Journal of Materials Processing Technology.

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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