World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
49
Citations
9208
World Ranking
4310
National Ranking
1239

Albert C. To publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Albert C. To sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 163 publications — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Albert C. To D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Albert C. To sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 49 D-Index — 57th percentile

57% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Albert C. To is affiliated with the University of Pittsburgh in the United States. Their work primarily spans the field of Engineering, with a strong focus on Mechanical Engineering and related subfields such as Automotive Engineering, Mechanics of Materials, Civil and Structural Engineering, and Industrial and Manufacturing Engineering.

The main topics of their research include Additive Manufacturing Materials and Processes, Additive Manufacturing and 3D Printing Technologies, Welding Techniques and Residual Stresses, Topology Optimization in Engineering, Composite Structure Analysis and Optimization, High Entropy Alloys Studies, and Manufacturing Process and Optimization.

Albert C. To has contributed extensively to the scientific literature, including recent publications such as:

  • Additive manufacturing of nickel-based superalloys: A state-of-the-art review on process-structure-defect-property relationship, 2023, Progress in Materials Science
  • A review of multi-scale and multi-physics simulations of metal additive manufacturing processes with focus on modeling strategies, 2021, Additive Manufacturing
  • Invited review: Machine learning for materials developments in metals additive manufacturing, 2020, Additive Manufacturing
  • Elucidating the effect of preheating temperature on melt pool morphology variation in Inconel 718 laser powder bed fusion via simulation and experiment, 2020, Additive Manufacturing
  • Homogenization timing effect on microstructure and precipitation strengthening of 17-4PH stainless steel fabricated by laser powder bed fusion, 2022, Additive Manufacturing

The venues where Albert C. To most frequently publishes include Additive Manufacturing, SSRN Electronic Journal, arXiv (Cornell University), Computer Methods in Applied Mechanics and Engineering, and JOM.

Among their frequent collaborators, several coauthors have worked with Albert C. To on multiple occasions, indicating established research partnerships. These include Hao Deng, Shawn Hinnebusch, Wen Dong, Qian Chen, and Xavier Jimenez.

Best Publications

  • Current and future trends in topology optimization for additive manufacturing

    Jikai Liu;Andrew T. Gaynor;Shikui Chen;Zhan Kang

  • Additive Manufacturing of Nickel-based superalloys: a state-of-the-art review on process-structure-defect-property relationship

    Unknown

  • Functionally graded lattice structure topology optimization for the design of additive manufactured components with stress constraints

    Lin Cheng;Jiaxi Bai;Albert C. To

  • Efficient Design-Optimization of Variable-Density Hexagonal Cellular Structure by Additive Manufacturing: Theory and Validation

    Pu Zhang;Jakub Toman;Yiqi Yu;Emre Biyikli

  • A review of multi-scale and multi-physics simulations of metal additive manufacturing processes with focus on modeling strategies

    Mohamad Bayat;Wen Dong;Jesper Thorborg;Albert C. To

  • Finite element modeling and validation of thermomechanical behavior of Ti-6Al-4V in directed energy deposition additive manufacturing

    Qingcheng Yang;Pu Zhang;Lin Cheng;Zheng Min

  • Interplay between phononic bandgaps and piezoelectric microstructures for energy harvesting

    Stefano Gonella;Albert C. To;Wing Kam Liu

  • Efficient design optimization of variable-density cellular structures for additive manufacturing: theory and experimental validation

    Lin Cheng;Pu Zhang;Emre Biyikli;Jiaxi Bai

  • An inherent strain based multiscale modeling framework for simulating part-scale residual deformation for direct metal laser sintering

    Qian Chen;Xuan Liang;Devlin Hayduke;Jikai Liu

  • A modified method for estimating inherent strains from detailed process simulation for fast residual distortion prediction of single-walled structures fabricated by directed energy deposition

    Xuan Liang;Lin Cheng;Qian Chen;Qingcheng Yang

  • Invited review: Machine learning for materials developments in metals additive manufacturing

    N.S. Johnson;N.S. Johnson;P.S. Vulimiri;A.C. To;X. Zhang

  • On utilizing topology optimization to design support structure to prevent residual stress induced build failure in laser powder bed metal additive manufacturing

    Lin Cheng;Xuan Liang;Jiaxi Bai;Qian Chen

  • Role of anisotropic properties on topology optimization of additive manufactured load bearing structures

    Pu Zhang;Pu Zhang;Jikai Liu;Jikai Liu;Albert C. To

  • Wavelet denoising techniques with applications to experimental geophysical data

    Albert C. To;Jeffrey R. Moore;Steven D. Glaser

  • Statistical volume element method for predicting microstructure-constitutive property relations

    Xiaolei Yin;Wei Chen;Albert To;Cahal McVeigh

  • Perfectly matched multiscale simulations

    Albert C. To;Shaofan Li

  • Biomimetic staggered composites with highly enhanced energy dissipation: Modeling, 3D printing, and testing

    Pu Zhang;Mary A. Heyne;Albert C. To

  • Quantitative texture prediction of epitaxial columnar grains in additive manufacturing using selective laser melting

    Jian Liu;Albert C. To

  • Broadband wave filtering of bioinspired hierarchical phononic crystal

    Pu Zhang;Albert C. To

  • Variation of hardness, microstructure, and Laves phase distribution in direct laser deposited alloy 718 cuboids

    Erica L. Stevens;Jakub Toman;Albert C. To;Markus Chmielus

  • Coupling lattice structure topology optimization with design-dependent feature evolution for additive manufactured heat conduction design

    Lin Cheng;Jikai Liu;Xuan Liang;Albert C. To

  • Machine Learning for Materials Developments in Metals Additive Manufacturing

    N. S. Johnson;P. S. Vulimiri;A. C. To;X. Zhang

Frequent Co-Authors

Wing Kam Liu
Wing Kam Liu Northwestern University
Kevin P. Chen
Kevin P. Chen University of Pittsburgh
Shaofan Li
Shaofan Li University of California, Berkeley
Bing-Yang Cao
Bing-Yang Cao Tsinghua University
Paul R. Ohodnicki
Paul R. Ohodnicki University of Pittsburgh
Ryan B. Wicker
Ryan B. Wicker The University of Texas at El Paso
Wei Chen
Wei Chen Northwestern University
Tengfei Luo
Tengfei Luo University of Notre Dame
Stephen R. Leone
Stephen R. Leone University of California, Berkeley
Ted Belytschko
Ted Belytschko Northwestern University

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Related Online Degrees & Career Pathways

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Exploring these pathways gives you more options for shaping your education and career around your goals—while taking advantage of the speed and convenience that online learning provides.

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