Wei Xu

5.7k total citations · 2 hit papers
198 papers, 3.9k citations indexed

About

Wei Xu is a scholar working on Mechanical Engineering, Materials Chemistry and Mechanics of Materials. According to data from OpenAlex, Wei Xu has authored 198 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 172 papers in Mechanical Engineering, 86 papers in Materials Chemistry and 59 papers in Mechanics of Materials. Recurrent topics in Wei Xu's work include Microstructure and Mechanical Properties of Steels (118 papers), Metal Alloys Wear and Properties (57 papers) and High Temperature Alloys and Creep (44 papers). Wei Xu is often cited by papers focused on Microstructure and Mechanical Properties of Steels (118 papers), Metal Alloys Wear and Properties (57 papers) and High Temperature Alloys and Creep (44 papers). Wei Xu collaborates with scholars based in China, Netherlands and Belgium. Wei Xu's co-authors include Sybrand van der Zwaag, Chenchong Wang, Jun Hu, Xiaolu Wei, Qi Lu, Xiaojun Xu, Pedro E.J. Rivera-Díaz-del-Castillo, Chunguang Shen, Lingyu Wang and D. San Martı́n and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and Acta Materialia.

In The Last Decade

Wei Xu

186 papers receiving 3.8k citations

Hit Papers

Physical metallurgy-guided machine learning and artificia... 2019 2026 2021 2023 2019 2024 50 100 150 200

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wei Xu China 37 3.2k 2.0k 1.1k 731 419 198 3.9k
Ulrich Prahl Germany 35 4.4k 1.4× 2.7k 1.3× 2.4k 2.2× 629 0.9× 525 1.3× 249 4.9k
Jingwei Zhao China 36 3.6k 1.1× 2.2k 1.1× 2.1k 1.8× 447 0.6× 678 1.6× 197 4.5k
C.J. Van Tyne United States 31 3.0k 0.9× 1.5k 0.7× 1.8k 1.6× 326 0.4× 267 0.6× 172 3.4k
Eralp Demir United Kingdom 19 2.3k 0.7× 1.7k 0.8× 1.1k 1.0× 396 0.5× 424 1.0× 41 2.9k
Sanbao Lin China 40 5.1k 1.6× 952 0.5× 743 0.7× 507 0.7× 1.5k 3.5× 221 5.6k
Ulrich Krupp Germany 33 2.9k 0.9× 1.6k 0.8× 1.6k 1.4× 989 1.4× 903 2.2× 227 3.8k
Xianghua Liu China 26 1.7k 0.5× 1.0k 0.5× 745 0.7× 210 0.3× 121 0.3× 173 2.0k
R. Viswanathan United States 28 3.2k 1.0× 1.5k 0.7× 1.2k 1.1× 562 0.8× 1.0k 2.5× 99 3.9k
Mohammad Jahazi Canada 48 7.9k 2.5× 3.6k 1.8× 2.6k 2.3× 505 0.7× 1.9k 4.6× 301 8.9k
Fuguo Li China 37 2.9k 0.9× 2.6k 1.3× 2.2k 2.0× 94 0.1× 671 1.6× 240 4.1k

Countries citing papers authored by Wei Xu

Since Specialization
Citations

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

Fields of papers citing papers by Wei Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wei Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Wei Xu. A scholar is included among the top collaborators of Wei Xu 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 Wei Xu. Wei Xu 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
1.
Chen, Jialin, et al.. (2025). The influence of reheated quenching on the retained austenite and mechanical properties of an ultra-high strength cryogenic steel. Materials Today Communications. 44. 112067–112067. 1 indexed citations
2.
Han, Siyu, et al.. (2025). Fitting-free mechanical response prediction in dual-phase steels by crystal plasticity theory guided deep learning. Acta Materialia. 289. 120936–120936. 6 indexed citations
4.
Jia, Mengyu, et al.. (2025). Efficient prediction of long-term wettability evolution in laser-textured surfaces using short-term data and cyclic deep learning. Optics & Laser Technology. 184. 112558–112558. 2 indexed citations
5.
Zhou, Z. Y., Shuize Wang, D. San Martı́n, et al.. (2025). A knowledge graph attention network for the cold‐start problem in intelligent manufacturing: Interpretability and accuracy improvement. SHILAP Revista de lepidopterología. 3(2).
6.
Li, Yizhuang, et al.. (2025). In-situ high modulus steel strengthened with Ni3Ti nano-precipitation. Materials Science and Engineering A. 935. 148355–148355.
7.
Tan, Fei, Peng Xing, Jing‐Kun Yan, et al.. (2024). Nondestructive detection of multiple qualities of dried jujube in different storage periods based on hyperspectral imaging combined with deep learning. Infrared Physics & Technology. 143. 105595–105595.
8.
Shen, Chunguang, Wangzhong Mu, Chenchong Wang, Wei Xu, & Peter Hedström. (2024). Uncovering the generic and alloy-specific governing parameters of deformation-induced martensitic transformation in austenitic steel. Journal of Materials Science. 59(7). 3087–3100. 4 indexed citations
9.
Chen, Yinping, Hao Yu, Hongshuang Di, & Wei Xu. (2024). Enhancing sulfur embrittlement resistance in Ni-based superalloys through synergistic effects of boron and carbon co-doping. Journal of Materials Science. 60(1). 445–462.
10.
11.
Yu, Hao, et al.. (2024). The design of oxidation resistant Ni superalloys for additive manufacturing. Additive manufacturing. 97. 104616–104616. 22 indexed citations
12.
Huang, Minghao, Lingyu Wang, Chenchong Wang, et al.. (2024). Optimizing crack initiation energy in austenitic steel via controlled martensitic transformation. Journal of Material Science and Technology. 198. 231–242. 59 indexed citations breakdown →
13.
Yu, Hao, Shiyi Chen, Lingyu Wang, et al.. (2024). The Dissolution Behaviors of Carbides During Solution Treatment for AMSC‐DB Ni‐Based Superalloys Fabricated by Laser‐Directed Energy Deposition and Forging. Advanced Engineering Materials. 26(22). 3 indexed citations
14.
Yu, Hao, Jianfeng Shao, Sansan Shuai, et al.. (2023). The application of self-healing concept in Ni superalloys: Theoretical design and experimental validation. Materials & Design. 237. 112587–112587. 2 indexed citations
15.
Yu, Hao, et al.. (2023). Prolonged creep lifetime of ferritic self-healing steels achieved by offline healing treatment. Materials Science and Engineering A. 873. 144959–144959. 2 indexed citations
16.
Xu, Ning, et al.. (2023). Promoting ductility and formability in a carbide free bainitic steel via pre-annealing treatment. Materials Characterization. 204. 113205–113205. 11 indexed citations
17.
18.
Huang, Minghao, Chenchong Wang, Lingyu Wang, et al.. (2022). Influence of DIMT on impact toughness: Relationship between crack propagation and the α′-martensite morphology in austenitic steel. Materials Science and Engineering A. 844. 143191–143191. 22 indexed citations
19.
Yu, Hao, Wei Xu, & Sybrand van der Zwaag. (2018). Connecting the Microstructure Stability of Ni Based Superalloys to their Chemical Compositions. Materials science forum. 941. 967–975. 2 indexed citations
20.
He, Binbin, Wei Xu, & Mingxin Huang. (2014). Increase of martensite start temperature after small deformation of austenite. Materials Science and Engineering A. 609. 141–146. 41 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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