Yingjie Zhang

617 total citations
39 papers, 463 citations indexed

About

Yingjie Zhang is a scholar working on Mechanical Engineering, Automotive Engineering and Industrial and Manufacturing Engineering. According to data from OpenAlex, Yingjie Zhang has authored 39 papers receiving a total of 463 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Mechanical Engineering, 11 papers in Automotive Engineering and 11 papers in Industrial and Manufacturing Engineering. Recurrent topics in Yingjie Zhang's work include Additive Manufacturing Materials and Processes (18 papers), Welding Techniques and Residual Stresses (11 papers) and Additive Manufacturing and 3D Printing Technologies (11 papers). Yingjie Zhang is often cited by papers focused on Additive Manufacturing Materials and Processes (18 papers), Welding Techniques and Residual Stresses (11 papers) and Additive Manufacturing and 3D Printing Technologies (11 papers). Yingjie Zhang collaborates with scholars based in China, Singapore and United States. Yingjie Zhang's co-authors include Dongsen Ye, Geok Soon Hong, Jerry Ying Hsi Fuh, Wentao Yan, Kunpeng Zhu, Di Wang, Xin Huang, Xiaodong Zhang, Yongqiang Yang and Han Wang and has published in prestigious journals such as Journal of Power Sources, IEEE Transactions on Industrial Informatics and Mechanical Systems and Signal Processing.

In The Last Decade

Yingjie Zhang

35 papers receiving 453 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yingjie Zhang China 12 348 191 133 51 49 39 463
Saigopal Nelaturi United States 13 223 0.6× 258 1.4× 258 1.9× 61 1.2× 41 0.8× 32 606
Alžbeta Sapietová Slovakia 13 323 0.9× 73 0.4× 140 1.1× 43 0.8× 40 0.8× 61 457
Haixi Wu China 6 286 0.8× 269 1.4× 215 1.6× 32 0.6× 33 0.7× 13 421
Guocai Ma China 9 280 0.8× 190 1.0× 107 0.8× 17 0.3× 154 3.1× 10 438
Lufeng Chen China 12 264 0.8× 217 1.1× 200 1.5× 138 2.7× 67 1.4× 26 488
Roudy Wehbe United States 10 211 0.6× 95 0.5× 102 0.8× 34 0.7× 41 0.8× 17 431
Zhendong Liu China 13 509 1.5× 73 0.4× 136 1.0× 28 0.5× 130 2.7× 38 660
John Stavridis Greece 10 276 0.8× 85 0.4× 162 1.2× 47 0.9× 12 0.2× 14 374
Dongsen Ye China 9 800 2.3× 505 2.6× 347 2.6× 87 1.7× 35 0.7× 10 918

Countries citing papers authored by Yingjie Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Yingjie Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yingjie Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Yingjie Zhang. A scholar is included among the top collaborators of Yingjie Zhang 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 Yingjie Zhang. Yingjie Zhang 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.
Ma, Chenguang, et al.. (2025). Twin-channel attention-based convolutional neural network for layer-wise prediction of surface roughness in the laser powder bed fusion process. Mechanical Systems and Signal Processing. 230. 112581–112581. 2 indexed citations
2.
Wang, Jinlin, Can Wu, Wang Zhang, et al.. (2025). Promoting polysulfide conversion by catalytic of ZnSe nanoparticles for room-temperature sodium-sulfur battery. Journal of Energy Storage. 119. 116374–116374. 1 indexed citations
3.
Deng, Yifan, et al.. (2024). Predicting and optimizing the dimensions of rod in lattice structures fabricated by laser powder bed fusion. Materials Today Communications. 40. 109979–109979. 4 indexed citations
4.
Zhang, Yingjie, et al.. (2024). A layer-wise melting defects mitigation method in laser powder bed fusion process based on machine learning and fuzzy inference. ISA Transactions. 156. 698–711. 5 indexed citations
5.
Ma, Chenguang, et al.. (2024). Layer-wise surface quality improvement in laser powder bed fusion through surface anomaly detection and control. Computers & Industrial Engineering. 191. 110098–110098. 11 indexed citations
6.
Chen, Xiaoqi, et al.. (2023). Digital twin temperature field prediction of laser powder bed fusion through proper orthogonal decomposition with radial basis function. Materials Today Communications. 38. 107883–107883. 3 indexed citations
7.
Zhang, Yingjie, et al.. (2023). Deep learning-based detection of part deformation for laser powder bed fusion process. abs/1706.05587. 153–159. 1 indexed citations
8.
Wang, Di, Han Wang, Zixin Liu, et al.. (2023). Influence mechanism of laser delay on internal defect and surface quality in stitching region of 316L stainless steel fabricated by dual-laser selective laser melting. Journal of Manufacturing Processes. 94. 35–48. 10 indexed citations
9.
Cai, Jian‐Feng, et al.. (2023). A lightweight high-resolution algorithm based on deep learning for layer-wise defect detection in laser powder bed fusion. Measurement Science and Technology. 35(2). 25604–25604. 13 indexed citations
10.
Wang, Di, et al.. (2023). Microphone signal specialities in laser powder bed fusion: single-track scan and multi-track scan. Journal of Materials Research and Technology. 24. 1344–1362. 6 indexed citations
11.
Ma, Chenguang, et al.. (2023). A deep convolutional network combining layerwise images and defect parameter vectors for laser powder bed fusion process anomalies classification. Journal of Intelligent Manufacturing. 35(6). 2929–2959. 14 indexed citations
12.
Huang, Xin, et al.. (2023). Intelligent fault diagnosis of turbine blade cracks via multiscale sparse filtering and multi-kernel support vector machine for information fusion. Advanced Engineering Informatics. 56. 101979–101979. 45 indexed citations
13.
Wang, Di, et al.. (2022). Microphone Signal Specialities in Laser Powder Bed Fusion: Single-Track Scan and Multi-Track Scan. SSRN Electronic Journal. 1 indexed citations
14.
Fan, Jinghao, et al.. (2022). Robot gluing localization method based on monocular vision. 25–25. 1 indexed citations
15.
Zhang, Yingjie, et al.. (2022). Investigation into the optical emission of features for powder-bed fusion AM process monitoring. The International Journal of Advanced Manufacturing Technology. 121(3-4). 2291–2303. 1 indexed citations
16.
Zhang, Yingjie, Kai Yang, & Jianping Zhao. (2020). Experimental Research and Numerical Simulation of Weld Repair with High Energy Spark Deposition Method. Metals. 10(7). 980–980. 3 indexed citations
17.
Liu, Yunda, Sheng Bi, Min Dong, et al.. (2018). A Reinforcement Learning Method for Humanoid Robot Walking. 623–628. 2 indexed citations
18.
Zhang, Yingjie, et al.. (2016). Learning Individual Behavior Using Sensor Data: The Case of GPS Traces and Taxi Drivers. SSRN Electronic Journal. 6 indexed citations
19.
Zhang, Yingjie & Liling Ge. (2008). Region-Based Image Fusion Approach Using Iterative Algorithm. 202–207. 3 indexed citations
20.
Zhang, Yingjie & Liling Ge. (2007). A Hierarchical Segmentation Algorithm Based on Mumford and Shah Model. 64 th. 735–740. 1 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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