Zhongfa Mao

1.4k total citations · 1 hit paper
27 papers, 1.1k citations indexed

About

Zhongfa Mao is a scholar working on Mechanical Engineering, Automotive Engineering and Biomedical Engineering. According to data from OpenAlex, Zhongfa Mao has authored 27 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Mechanical Engineering, 14 papers in Automotive Engineering and 5 papers in Biomedical Engineering. Recurrent topics in Zhongfa Mao's work include Additive Manufacturing and 3D Printing Technologies (14 papers), Additive Manufacturing Materials and Processes (13 papers) and Welding Techniques and Residual Stresses (6 papers). Zhongfa Mao is often cited by papers focused on Additive Manufacturing and 3D Printing Technologies (14 papers), Additive Manufacturing Materials and Processes (13 papers) and Welding Techniques and Residual Stresses (6 papers). Zhongfa Mao collaborates with scholars based in China, United Kingdom and Canada. Zhongfa Mao's co-authors include David Z. Zhang, Junjie Jiang, Peng Zhang, Zhibo Ma, Dan M. Spengler, T. S. Keller, Fei Liu, Guang Fu, Xiaodong Niu and Kaifei Zhang and has published in prestigious journals such as Materials Science and Engineering A, Advanced Science and Environmental Science and Pollution Research.

In The Last Decade

Zhongfa Mao

26 papers receiving 1.1k citations

Hit Papers

Functionally graded porous scaffolds in multiple patterns... 2018 2026 2020 2023 2018 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhongfa Mao China 14 790 466 267 207 124 27 1.1k
Michele Dallago Italy 16 1.1k 1.4× 762 1.6× 293 1.1× 221 1.1× 65 0.5× 35 1.5k
Gianni Campoli Netherlands 13 646 0.8× 429 0.9× 448 1.7× 192 0.9× 263 2.1× 16 1.1k
Noel M. Harrison Ireland 25 833 1.1× 673 1.4× 232 0.9× 201 1.0× 188 1.5× 75 1.6k
Grzegorz Pyka Belgium 14 1.1k 1.4× 797 1.7× 607 2.3× 212 1.0× 153 1.2× 48 1.6k
Erica Liverani Italy 19 1.6k 2.0× 870 1.9× 276 1.0× 221 1.1× 90 0.7× 62 1.8k
Shaaz Ghouse United Kingdom 13 505 0.6× 355 0.8× 290 1.1× 137 0.7× 149 1.2× 17 764
Siyuan Pang United States 10 319 0.4× 278 0.6× 232 0.9× 71 0.3× 41 0.3× 14 655
Maziar Ramezani New Zealand 23 1000 1.3× 342 0.7× 396 1.5× 453 2.2× 159 1.3× 128 1.8k
Alejandro Yánez Spain 10 430 0.5× 307 0.7× 274 1.0× 130 0.6× 149 1.2× 25 657
Daniel Koutný Czechia 20 757 1.0× 541 1.2× 206 0.8× 117 0.6× 65 0.5× 59 1.1k

Countries citing papers authored by Zhongfa Mao

Since Specialization
Citations

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

Fields of papers citing papers by Zhongfa Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhongfa Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Zhongfa Mao. A scholar is included among the top collaborators of Zhongfa Mao 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 Zhongfa Mao. Zhongfa Mao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
2.
Yang, Nan, Lan Yu, Miao Zhao, et al.. (2024). Bistable Soft Shells for Programmable Mechanical Logic. Advanced Science. 12(5). e2412372–e2412372. 8 indexed citations
3.
Mao, Zhongfa, et al.. (2023). Energy absorption prediction for lattice structure based on D2 shape distribution and machine learning. Composite Structures. 319. 117136–117136. 7 indexed citations
4.
Ji, Xiulin, et al.. (2023). Effect of Nb Addition on the Corrosion and Wear Resistance of Laser Clad AlCr2FeCoNi High-Entropy Alloy Coatings. Lubricants. 12(1). 5–5. 12 indexed citations
5.
Peng, Shitong, et al.. (2023). Generative machine learning-based multi-objective process parameter optimization towards energy and quality of injection molding. Environmental Science and Pollution Research. 30(18). 51518–51530. 18 indexed citations
7.
He, Ke, et al.. (2022). Effect of Microstructural Anisotropy on Fracture Toughness of Selective‐Laser‐Melted 15‐5 PH Stainless Steel. steel research international. 94(3). 5 indexed citations
8.
Yang, Nan, et al.. (2022). Tuning surface curvatures and young's moduli of TPMS-based lattices independent of volume fraction. Materials & Design. 216. 110542–110542. 25 indexed citations
9.
Yang, Nan, et al.. (2022). Largely Tuning Geometrical and Mechanical Properties of Tpms-Based Lattices Independent of Volume Fraction. SSRN Electronic Journal. 1 indexed citations
10.
Rao, Longshi, et al.. (2021). Solvent regulation synthesis of single-component white emission carbon quantum dots for white light-emitting diodes. Nanotechnology Reviews. 10(1). 465–477. 24 indexed citations
11.
Mao, Zhongfa, et al.. (2021). Processing optimization, microstructure, mechanical properties and nanoprecipitation behavior of 18Ni300 maraging steel in selective laser melting. Materials Science and Engineering A. 830. 142334–142334. 53 indexed citations
12.
Jiang, Junjie, Jianming Chen, Zhihao Ren, et al.. (2020). The Influence of Process Parameters and Scanning Strategy on Lower Surface Quality of TA15 Parts Fabricated by Selective Laser Melting. Metals. 10(9). 1228–1228. 17 indexed citations
13.
Jiang, Junjie, Zhihao Ren, Zhibo Ma, et al.. (2019). Mechanical properties and microstructural evolution of TA15 Ti alloy processed by selective laser melting before and after annealing. Materials Science and Engineering A. 772. 138742–138742. 110 indexed citations
14.
Wang, Li, Xiaodong Niu, Jian Zhang, et al.. (2019). Study of Effective Hinge Thickness of Additive-manufactured Flexure Mechanisms. 307–311. 1 indexed citations
15.
Zhang, Kaifei, Guang Fu, Peng Zhang, et al.. (2018). Study on the Geometric Design of Supports for Overhanging Structures Fabricated by Selective Laser Melting. Materials. 12(1). 27–27. 38 indexed citations
16.
Zhang, Kaifei, Zhongfa Mao, Guang Fu, et al.. (2018). A feasible method of support slimming based on the different thresholds of polar angles in selective laser melting. Materials & Design. 157. 501–511. 9 indexed citations
17.
Mao, Zhongfa, David Z. Zhang, Junjie Jiang, Guang Fu, & Peng Zhang. (2018). Processing optimisation, mechanical properties and microstructural evolution during selective laser melting of Cu-15Sn high-tin bronze. Materials Science and Engineering A. 721. 125–134. 124 indexed citations
18.
Zhang, Peng, et al.. (2018). Rolling contact fatigue performance evaluation of Ti–6Al–4V parts processed by selective laser melting. The International Journal of Advanced Manufacturing Technology. 96(9-12). 3533–3543. 11 indexed citations
19.
Mao, Zhongfa, David Z. Zhang, Peitang Wei, & Kaifei Zhang. (2017). Manufacturing Feasibility and Forming Properties of Cu-4Sn in Selective Laser Melting. Materials. 10(4). 333–333. 53 indexed citations
20.
Keller, T. S., Zhongfa Mao, & Dan M. Spengler. (1990). Young's modulus, bending strength, and tissue physical properties of human compact bone. Journal of Orthopaedic Research®. 8(4). 592–603. 177 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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