Seong-Hoon Kang

1.6k total citations
98 papers, 1.2k citations indexed

About

Seong-Hoon Kang is a scholar working on Mechanical Engineering, Materials Chemistry and Mechanics of Materials. According to data from OpenAlex, Seong-Hoon Kang has authored 98 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Mechanical Engineering, 35 papers in Materials Chemistry and 30 papers in Mechanics of Materials. Recurrent topics in Seong-Hoon Kang's work include Metallurgy and Material Forming (24 papers), Metal Forming Simulation Techniques (17 papers) and Semiconductor materials and devices (13 papers). Seong-Hoon Kang is often cited by papers focused on Metallurgy and Material Forming (24 papers), Metal Forming Simulation Techniques (17 papers) and Semiconductor materials and devices (13 papers). Seong-Hoon Kang collaborates with scholars based in South Korea, Japan and China. Seong-Hoon Kang's co-authors include Yong‐Taek Im, Ho Won Lee, J. K. Lee, Se‐Jong Kim, Jae‐Seung Cheon, Youngseok Oh, Seong‐Whan Lee, Hyun Wook Lee, Seung‐Chul Lee and G.S. Yun and has published in prestigious journals such as Applied Physics Letters, Acta Materialia and Scientific Reports.

In The Last Decade

Seong-Hoon Kang

90 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Seong-Hoon Kang South Korea 20 468 387 277 264 215 98 1.2k
Henrik Myhre Jensen Denmark 21 681 1.5× 347 0.9× 1.6k 5.6× 176 0.7× 45 0.2× 88 2.1k
Yanpeng Hao China 21 618 1.3× 761 2.0× 160 0.6× 915 3.5× 29 0.1× 162 1.8k
Liang Hu China 19 257 0.5× 161 0.4× 324 1.2× 368 1.4× 27 0.1× 114 1.1k
Suman Shrestha United States 18 496 1.1× 593 1.5× 227 0.8× 146 0.6× 38 0.2× 83 1.2k
Assimina A. Pelegri United States 15 282 0.6× 193 0.5× 652 2.4× 65 0.2× 100 0.5× 84 1.1k
Peng Huang China 21 656 1.4× 102 0.3× 129 0.5× 262 1.0× 31 0.1× 102 1.3k
J. Meijer Netherlands 18 1.0k 2.2× 138 0.4× 370 1.3× 285 1.1× 14 0.1× 96 1.6k
Ching‐Hsiang Cheng Hong Kong 19 146 0.3× 59 0.2× 380 1.4× 554 2.1× 380 1.8× 76 1.2k
Suwas Nikumb Canada 18 353 0.8× 314 0.8× 253 0.9× 445 1.7× 18 0.1× 91 1.4k
Dawei Wu China 23 217 0.5× 381 1.0× 355 1.3× 305 1.2× 345 1.6× 117 1.6k

Countries citing papers authored by Seong-Hoon Kang

Since Specialization
Citations

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

Fields of papers citing papers by Seong-Hoon Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seong-Hoon Kang. 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 Seong-Hoon Kang. The network helps show where Seong-Hoon Kang may publish in the future.

Co-authorship network of co-authors of Seong-Hoon Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Seong-Hoon Kang. A scholar is included among the top collaborators of Seong-Hoon Kang 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 Seong-Hoon Kang. Seong-Hoon Kang 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.
Tran, Minh Tien, et al.. (2026). Prediction of residual stress in additively manufactured 18Ni300 maraging steel using GAN-based deep learning method. Journal of Materials Research and Technology. 41. 1703–1715.
2.
Tran, Minh Tien, Hyunki Kim, Kyung‐Hwan Jung, et al.. (2025). Crystal plasticity modeling of ductile fracture locus in advanced high-strength steels. Results in Engineering. 27. 105720–105720.
3.
Kang, Seong-Hoon, et al.. (2025). BalanceVR: balance training to increase tolerance to cybersickness in immersive virtual reality. Virtual Reality. 29(1). 3 indexed citations
4.
Tran, Minh Tien, Ho Won Lee, Seong-Hoon Kang, et al.. (2025). Physics-guided machine learning for forming-limit assessments of advanced high-strength steels. International Journal of Mechanical Sciences. 287. 109959–109959. 4 indexed citations
5.
Lee, Wonjoo, Yong‐Taek Hyun, Jong Woo Won, et al.. (2025). Numerical simulation using a coupled lattice Boltzmann–cellular automata method to predict the microstructure of Ti–6Al–4V after electron beam cold hearth melting. Journal of Materials Research and Technology. 36. 3796–3806.
6.
Jung, Jaimyun, et al.. (2025). Modeling the relationship between tempering process-microstructure-property of steel using deep learning. Computational Materials Science. 260. 114246–114246. 1 indexed citations
7.
Tran, Minh Tien, Hyunki Kim, Hobyung Chae, et al.. (2024). Correlation between individual phase constitutive properties and plastic heterogeneities in advanced-high strength dual-phase steels. Materials Characterization. 217. 114356–114356. 8 indexed citations
8.
Wang, Huai, Ho Won Lee, Seong-Hoon Kang, & Dong‐Kyu Kim. (2024). Crystal Plasticity Finite Element Analyses on the Formability of AA6061 Aluminum Alloy with Different Ageing Treatments. Metals. 14(5). 503–503. 5 indexed citations
9.
Tran, Minh Tien, et al.. (2024). Ductile fracture locus under various deformation modes with negative-to-positive stress triaxiality. International Journal of Mechanical Sciences. 279. 109615–109615. 8 indexed citations
10.
Lee, Jaejun, et al.. (2023). Label-free grain segmentation for optical microscopy images via unsupervised image-to-image translation. Materials Characterization. 206. 113410–113410. 9 indexed citations
11.
Kim, Se‐Jong, et al.. (2023). A unified microstructure segmentation approach via human-in-the-loop machine learning. Acta Materialia. 255. 119086–119086. 19 indexed citations
12.
Jeong, Hi Won, et al.. (2022). Predicting High Temperature Flow Stress of Nickel Alloy A230 Based on an Artificial Neural Network. Metals. 12(2). 223–223. 10 indexed citations
13.
Kang, Seong-Hoon, et al.. (2020). Radiation exposure and fluoroscopically-guided interventional procedures among orthopedic surgeons in South Korea. Journal of Occupational Medicine and Toxicology. 15(1). 24–24. 15 indexed citations
14.
Ko, Seulki, Seong-Hoon Kang, Mina Ha, et al.. (2018). Health Effects from Occupational Radiation Exposure among Fluoroscopy-Guided Interventional Medical Workers: A Systematic Review. Journal of Vascular and Interventional Radiology. 29(3). 353–366. 46 indexed citations
15.
Kang, Seong-Hoon, et al.. (2018). Thyroid cancer among female workers in Korea, 2007–2015. Annals of Occupational and Environmental Medicine. 30(1). 48–48. 2 indexed citations
16.
Kang, Seong-Hoon, et al.. (2018). The accuracy of analyzing reverberation time. The Journal of the Acoustical Society of Korea. 37(5). 349–355. 1 indexed citations
17.
Kang, Seong-Hoon, et al.. (2016). Relating factors to wearing personal radiation protectors among healthcare professionals. Annals of Occupational and Environmental Medicine. 28(1). 60–60. 1 indexed citations
18.
Kim, Jin Hee, Dong-Hoon Jung, Jin Young Kang, et al.. (2007). A Highly Reliable FRAM (Ferroelectric Random Access Memory). 95. 554–557. 5 indexed citations
19.
Song, Youjian, H. J. Joo, Seong-Hoon Kang, et al.. (2004). Electrical properties of highly reliable 32Mb FRAM with advanced capacitor technology. Microelectronics Reliability. 45(7-8). 1150–1153. 5 indexed citations
20.
Kang, Seong-Hoon, Hyeran Byun, & Seong‐Whan Lee. (2003). REAL-TIME PEDESTRIAN DETECTION USING SUPPORT VECTOR MACHINES. International Journal of Pattern Recognition and Artificial Intelligence. 17(3). 405–416. 16 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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