Mojtaba Mozaffar

1.6k total citations · 1 hit paper
13 papers, 1.2k citations indexed

About

Mojtaba Mozaffar is a scholar working on Mechanical Engineering, Automotive Engineering and Industrial and Manufacturing Engineering. According to data from OpenAlex, Mojtaba Mozaffar has authored 13 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Mechanical Engineering, 6 papers in Automotive Engineering and 5 papers in Industrial and Manufacturing Engineering. Recurrent topics in Mojtaba Mozaffar's work include Additive Manufacturing Materials and Processes (6 papers), Additive Manufacturing and 3D Printing Technologies (6 papers) and Manufacturing Process and Optimization (4 papers). Mojtaba Mozaffar is often cited by papers focused on Additive Manufacturing Materials and Processes (6 papers), Additive Manufacturing and 3D Printing Technologies (6 papers) and Manufacturing Process and Optimization (4 papers). Mojtaba Mozaffar collaborates with scholars based in United States, Switzerland and Hong Kong. Mojtaba Mozaffar's co-authors include Jian Cao, Kornel F. Ehmann, Miguel A. Bessa, Ramin Bostanabad, Maysam B. Gorji, Dirk Mohr, Julian N. Heidenreich, Sarah J. Wolff, Shuheng Liao and Wing Kam Liu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the Mechanics and Physics of Solids and Journal of Materials Processing Technology.

In The Last Decade

Mojtaba Mozaffar

13 papers receiving 1.1k citations

Hit Papers

Deep learning predicts path-dependent plasticity 2019 2026 2021 2023 2019 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep learning predicts path-dependent plasticity
    2019 410 citations Mojtaba Mozaffar, Ramin Bostanabad et al. Proceedings of the National Academy of Sciences profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mojtaba Mozaffar United States 10 654 409 275 266 210 13 1.2k
Orion L. Kafka United States 18 1.1k 1.7× 390 1.0× 647 2.4× 300 1.1× 102 0.5× 43 1.6k
Navid Zobeiry United States 23 724 1.1× 949 2.3× 149 0.5× 125 0.5× 172 0.8× 63 1.6k
Zhengtao Gan United States 20 1.7k 2.6× 224 0.5× 743 2.7× 363 1.4× 103 0.5× 32 2.1k
Jacob Hochhalter United States 19 572 0.9× 449 1.1× 94 0.3× 239 0.9× 35 0.2× 65 1.2k
Zhencai Zhu China 22 949 1.5× 412 1.0× 144 0.5× 158 0.6× 26 0.1× 99 1.4k
К. В. Аврамов Ukraine 21 323 0.5× 533 1.3× 83 0.3× 202 0.8× 96 0.5× 138 1.3k
Sourav Saha Bangladesh 21 592 0.9× 147 0.4× 80 0.3× 259 1.0× 102 0.5× 54 1.1k
Hiroyuki Sugiyama Japan 27 1.3k 1.9× 682 1.7× 326 1.2× 106 0.4× 28 0.1× 123 2.5k
Jiaying Gao United States 15 341 0.5× 716 1.8× 42 0.2× 111 0.4× 119 0.6× 25 1.1k
Stewart McWilliam United Kingdom 24 481 0.7× 423 1.0× 41 0.1× 165 0.6× 44 0.2× 62 1.5k

Countries citing papers authored by Mojtaba Mozaffar

Since Specialization
Citations

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

Fields of papers citing papers by Mojtaba Mozaffar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mojtaba Mozaffar

This figure shows the co-authorship network connecting the top 25 collaborators of Mojtaba Mozaffar. A scholar is included among the top collaborators of Mojtaba Mozaffar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mojtaba Mozaffar. Mojtaba Mozaffar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Cao, Jian, Markus Bambach�, Marion Merklein, Mojtaba Mozaffar, & Tianju Xue. (2024). Artificial intelligence in metal forming. CIRP Annals. 73(2). 561–587. 19 indexed citations
2.
Liao, Shuheng, et al.. (2023). Efficient GPU-accelerated thermomechanical solver for residual stress prediction in additive manufacturing. Computational Mechanics. 71(5). 879–893. 14 indexed citations
3.
Mozaffar, Mojtaba, Shuheng Liao, Jihoon Jeong, Tianju Xue, & Jian Cao. (2022). Differentiable simulation for material thermal response design in additive manufacturing processes. Additive manufacturing. 61. 103337–103337. 6 indexed citations
4.
Mozaffar, Mojtaba, Shuheng Liao, Ji-Hoon Jeong, Tianju Xue, & Jian Cao. (2022). Differentiable Simulation for Material Thermal Response Design in Additive Manufacturing Processes. SSRN Electronic Journal. 6 indexed citations
5.
Mozaffar, Mojtaba, Shuheng Liao, Xiaoyu Xie, et al.. (2021). Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives. Journal of Materials Processing Technology. 302. 117485–117485. 88 indexed citations
6.
Mozaffar, Mojtaba, Shuheng Liao, Hui Lin, Kornel F. Ehmann, & Jian Cao. (2021). Geometry-agnostic data-driven thermal modeling of additive manufacturing processes using graph neural networks. Additive manufacturing. 48. 102449–102449. 48 indexed citations
7.
Gorji, Maysam B., Mojtaba Mozaffar, Julian N. Heidenreich, Jian Cao, & Dirk Mohr. (2020). On the potential of recurrent neural networks for modeling path dependent plasticity. Journal of the Mechanics and Physics of Solids. 143. 103972–103972. 202 indexed citations
8.
Moser, Newell, et al.. (2019). Improving the accuracy of double-sided incremental forming simulations by considering kinematic hardening and machine compliance. Procedia Manufacturing. 29. 88–95. 11 indexed citations
9.
Mozaffar, Mojtaba, et al.. (2019). Deep learning predicts path-dependent plasticity. Proceedings of the National Academy of Sciences. 116(52). 26414–26420. 410 indexed citations breakdown →
10.
Mozaffar, Mojtaba, Ebot Ndip-Agbor, Stephen Lin, et al.. (2019). Acceleration strategies for explicit finite element analysis of metal powder-based additive manufacturing processes using graphical processing units. Computational Mechanics. 64(3). 879–894. 31 indexed citations
11.
Garcia, Daniel J., Mojtaba Mozaffar, Huaqing Ren, et al.. (2018). Sustainable Manufacturing With Cyber-Physical Discrete Manufacturing Networks: Overview and Modeling Framework. Journal of Manufacturing Science and Engineering. 141(2). 9 indexed citations
12.
Yan, Wentao, Stephen Lin, Orion L. Kafka, et al.. (2018). Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing. Computational Mechanics. 61(5). 521–541. 176 indexed citations
13.
Mozaffar, Mojtaba, Arindam Paul, Reda Al-Bahrani, et al.. (2018). Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manufacturing Letters. 18. 35–39. 148 indexed citations

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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