Sunghoon Lim

1.4k total citations · 1 hit paper
38 papers, 855 citations indexed

About

Sunghoon Lim is a scholar working on Mechanical Engineering, Industrial and Manufacturing Engineering and Artificial Intelligence. According to data from OpenAlex, Sunghoon Lim has authored 38 papers receiving a total of 855 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Mechanical Engineering, 10 papers in Industrial and Manufacturing Engineering and 8 papers in Artificial Intelligence. Recurrent topics in Sunghoon Lim's work include Advanced machining processes and optimization (8 papers), Advanced Machining and Optimization Techniques (7 papers) and Industrial Vision Systems and Defect Detection (7 papers). Sunghoon Lim is often cited by papers focused on Advanced machining processes and optimization (8 papers), Advanced Machining and Optimization Techniques (7 papers) and Industrial Vision Systems and Defect Detection (7 papers). Sunghoon Lim collaborates with scholars based in South Korea, United States and Canada. Sunghoon Lim's co-authors include Bayu Adhi Tama, Gyeongho Kim, Conrad S. Tucker, Jae Gyeong Choi, Malinda Vania, Seung‐Chul Lee, Soundar Kumara, Hyung Wook Park, Young-Jae Park and Hyewon Cho and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and International Journal of Production Research.

In The Last Decade

Sunghoon Lim

35 papers receiving 826 citations

Hit Papers

align trajectories

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.

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

2022 147 citations Sunghoon Lim et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sunghoon Lim South Korea	17	237	219	195	112	111	38	855
Melinda Hodkiewicz Australia	22	240 1.0×	199 0.9×	236 1.2×	150 1.3×	99 0.9×	95	1.2k
Guang Wang United States	19	166 0.7×	145 0.7×	161 0.8×	113 1.0×	307 2.8×	95	1.1k
Roman Tkachenko Ukraine	18	281 1.2×	64 0.3×	110 0.6×	60 0.5×	102 0.9×	68	856
Chao Yu China	22	458 1.9×	209 1.0×	231 1.2×	29 0.3×	160 1.4×	85	1.4k
Wenzhe Li China	13	255 1.1×	71 0.3×	124 0.6×	44 0.4×	192 1.7×	52	752
Chi‐Wei Lin Taiwan	16	124 0.5×	357 1.6×	55 0.3×	105 0.9×	94 0.8×	46	881
Zeashan Hameed Khan Pakistan	15	143 0.6×	94 0.4×	200 1.0×	169 1.5×	155 1.4×	79	1.1k
Wolfgang Reif Germany	17	431 1.8×	144 0.7×	196 1.0×	206 1.8×	49 0.4×	182	1.1k
Tao Tang China	14	136 0.6×	70 0.3×	89 0.5×	81 0.7×	64 0.6×	61	541
Kai Gao China	17	147 0.6×	122 0.6×	262 1.3×	64 0.6×	205 1.8×	98	1.1k

Countries citing papers authored by Sunghoon Lim

Since Specialization

Citations

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

Fields of papers citing papers by Sunghoon Lim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sunghoon Lim

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Kim, Gyeongho, et al.. (2025). Towards efficient data-driven fault diagnosis under low-budget scenarios via hybrid deep active learning. Reliability Engineering & System Safety. 266. 111637–111637.

Choi, Jae Gyeong, et al.. (2025). Artificial intelligence augmented digital twin for improving the machinability in a robotic carbon fiber reinforced plastics machining process. Journal of Intelligent Manufacturing. 1 indexed citations

Kim, Gyeongho, et al.. (2024). Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V. Journal of Manufacturing Systems. 76. 133–157. 13 indexed citations

Park, Soyeon, et al.. (2024). Applying Multistep Classification Techniques With Pre-Classification to Recognize Static and Dynamic Hand Gestures Using a Soft Sensor-Embedded Glove. IEEE Sensors Journal. 24(19). 30668–30679.

Choi, Jae Gyeong, et al.. (2024). Multimodal 1D CNN for delamination prediction in CFRP drilling process with industrial robots. Computers & Industrial Engineering. 190. 110074–110074. 14 indexed citations

Choi, Jae Gyeong, et al.. (2024). Accurate synthesis of sensor-to-machined-surface image generation in carbon fiber-reinforced plastic drilling. Expert Systems with Applications. 255. 124656–124656. 2 indexed citations

Kim, Gyeongho, Jae Gyeong Choi, & Sunghoon Lim. (2024). Using transformer and a reweighting technique to develop a remaining useful life estimation method for turbofan engines. Engineering Applications of Artificial Intelligence. 133. 108475–108475. 24 indexed citations

Kim, Gyeongho, et al.. (2024). Developing a data-driven system for grinding process parameter optimization using machine learning and metaheuristic algorithms. CIRP journal of manufacturing science and technology. 51. 20–35. 17 indexed citations

Kim, Gyeongho, et al.. (2024). Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions. Reliability Engineering & System Safety. 253. 110549–110549. 13 indexed citations

10.

Choi, Jae Gyeong, et al.. (2024). Multimodal sensor-to-machined surface image diffusion for defect detection in industrial processes. Annual Conference of the PHM Society. 16(1). 1 indexed citations

11.

Singh, Thipendra P., et al.. (2023). Social Media and Crowdsourcing. Auerbach Publications eBooks. 1 indexed citations

12.

Lim, Sunghoon, et al.. (2022). A TOPSIS-Inspired Ranking Method Using Constrained Crowd Opinions for Urban Planning. Entropy. 24(3). 371–371. 4 indexed citations

13.

Kim, Gyeongho, et al.. (2022). Tool Wear Prediction in the End Milling Process of Ti-6Al-4V using Bayesian Learning. 64–69. 1 indexed citations

14.

Hwang, Seong Wook & Sunghoon Lim. (2022). The charging infrastructure design problem with electric taxi demand prediction using convolutional LSTM. European J of Industrial Engineering. 16(6). 651–651. 2 indexed citations

15.

Kim, Gyeongho, et al.. (2022). A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data. IEEE Access. 10. 56232–56248. 47 indexed citations

16.

Lim, Sunghoon, Sun Jun Kim, Young-Jae Park, & Nahyun Kwon. (2021). A deep learning-based time series model with missing value handling techniques to predict various types of liquid cargo traffic. Expert Systems with Applications. 184. 115532–115532. 24 indexed citations

17.

Choi, Jae Gyeong, et al.. (2021). Car crash detection using ensemble deep learning and multimodal data from dashboard cameras. Expert Systems with Applications. 183. 115400–115400. 50 indexed citations

18.

Kim, Gyeongho, et al.. (2021). A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process. IEEE Access. 9. 132455–132467. 32 indexed citations

19.

Lim, Sunghoon, Conrad S. Tucker, Kathryn Jablokow, & Bart Pursel. (2018). A semantic network model for measuring engagement and performance in online learning platforms. Computer Applications in Engineering Education. 26(5). 1481–1492. 19 indexed citations

20.

Lim, Sunghoon, Conrad S. Tucker, & Soundar Kumara. (2016). An unsupervised machine learning model for discovering latent infectious diseases using social media data. Journal of Biomedical Informatics. 66. 82–94. 85 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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