World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
8686
World Ranking
8295
National Ranking
3557

Dazhong Wu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Dazhong Wu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 85 publications — 4th percentile

4% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Dazhong Wu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Dazhong Wu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Dazhong Wu is a researcher affiliated with the University of Central Florida in the United States, with a focus on engineering disciplines. Their work spans multiple subfields including mechanical engineering, automotive engineering, control and systems engineering, electrical and electronic engineering, and biomedical engineering.

The core areas of Wu's research involve additive manufacturing and 3D printing technologies, additive manufacturing materials and processes, advanced battery technologies research, machine fault diagnosis techniques, advancements in battery materials, reliability and maintenance optimization, and cellular and composite structures.

Wu has published extensively, with a total of 125 research outputs categorized under engineering. Major publication venues include:

  • Materials & Design
  • Reliability Engineering & System Safety
  • Advanced Engineering Informatics
  • SSRN Electronic Journal
  • Mechanical Systems and Signal Processing

Their research collaborations often involve the following frequent co-authors:

  • Qingyang Liu
  • Yupeng Wei
  • Janis Terpenny
  • Junchuan Shi
  • Denizhan Yavaş

Recent papers authored or co-authored by Wu highlight applications of advanced machine learning and manufacturing techniques in engineering contexts:

  • Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks, 2021, Mechanical Systems and Signal Processing
  • Prediction of melt pool temperature in directed energy deposition using machine learning, 2020, Additive Manufacturing
  • Interlaminar shear behavior of continuous and short carbon fiber reinforced polymer composites fabricated by additive manufacturing, 2020, Composites Part B Engineering
  • Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms, 2022, Reliability Engineering & System Safety
  • Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction, 2022, Mechanical Systems and Signal Processing

Best Publications

  • Deep learning for smart manufacturing: Methods and applications

    Jinjiang Wang;Yulin Ma;Laibin Zhang;Robert X. Gao

  • Cloud-based design and manufacturing

    Dazhong Wu;David W. Rosen;Lihui Wang;Dirk Schaefer

  • Cloud manufacturing: Strategic vision and state-of-the-art☆

    Dazhong Wu;Matthew John Greer;David W. Rosen;Dirk Schaefer

  • A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

    Dazhong Wu;Connor Jennings;Janis Terpenny;Robert X. Gao

  • Prediction of surface roughness in extrusion-based additive manufacturing with machine learning

    Zhixiong Li;Ziyang Zhang;Junchuan Shi;Dazhong Wu

  • A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing

    Dazhong Wu;Shaopeng Liu;Li Zhang;Janis Terpenny

  • Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks

    Junchuan Shi;Dikang Peng;Zhongxiao Peng;Ziyang Zhang

  • TOWARDS A CLOUD-BASED DESIGN AND MANUFACTURING PARADIGM: LOOKING BACKWARD, LOOKING FORWARD

    Dazhong Wu;J. Lane Thames;David W. Rosen;Dirk Schaefer

  • Enhancing the Product Realization Process With Cloud-Based Design and Manufacturing Systems

    Dazhong Wu;J. Lane Thames;David W. Rosen;Dirk Schaefer

  • Cybersecurity for digital manufacturing

    Dazhong Wu;Anqi Ren;Wenhui Zhang;Feifei Fan

  • An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction

    Zhixiong Li;Dazhong Wu;Chao Hu;Janis P. Terpenny

  • Prediction of melt pool temperature in directed energy deposition using machine learning

    Ziyang Zhang;Zhichao Liu;Dazhong Wu

  • Cloud-Based Manufacturing: Old Wine in New Bottles?

    Dazhong Wu;David W. Rosen;Lihui Wang;Dirk Schaefer

  • Fracture behavior of 3D printed carbon fiber-reinforced polymer composites

    Denizhan Yavas;Ziyang Zhang;Qingyang Liu;Dazhong Wu

  • Predictive modelling of surface roughness in fused deposition modelling using data fusion

    Dazhong Wu;Yupeng Wei;Janis P. Terpenny

  • Cloud Manufacturing: Drivers, Current Status, and Future Trends

    Dazhong Wu;Matthew J. Greer;David W. Rosen;Dirk Schaefer

  • Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning

    Zhixiong Li;Kai Goebel;Kai Goebel;Dazhong Wu

  • Predictive Modeling of Droplet Formation Processes in Inkjet-Based Bioprinting

    Dazhong Wu;Changxue Xu

  • Cloud-Based Design and Manufacturing: Status and Promise

    Dazhong Wu;David W. Rosen;Dirk Schaefer

  • Forecasting Obsolescence Risk and Product Life Cycle With Machine Learning

    Connor Jennings;Dazhong Wu;Janis Terpenny

  • DISTRIBUTED COLLABORATIVE DESIGN AND MANUFACTURE IN THE CLOUD — MOTIVATION, INFRASTRUCTURE, AND EDUCATION

    Dirk Schaefer;J Lane Thames;Robert D Wellman;Dazhong Wu

Frequent Co-Authors

David W. Rosen
David W. Rosen Singapore University of Technology and Design
Kai Goebel
Kai Goebel Palo Alto Research Center
Robert X. Gao
Robert X. Gao Case Western Reserve University
Thomas R. Kurfess
Thomas R. Kurfess Oak Ridge National Laboratory
Soundar R. T. Kumara
Soundar R. T. Kumara Pennsylvania State University
Lihui Wang
Lihui Wang Royal Institute of Technology
Chao Hu
Chao Hu Iowa State University
Xi Liu
Xi Liu Shanghai Jiao Tong University
Yongho Sohn
Yongho Sohn University of Central Florida
Albert J. Shih
Albert J. Shih University of Michigan–Ann Arbor

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