Byung Gun Joung
Engineering · Purdue University West Lafayette
Publications
13
Citations
161
Est. group size
~2
Recurring co-author estimate
Active years
11
Publishing since 2016
Byung Gun Joung's work focuses on smart and sustainable manufacturing, including using machine learning to detect equipment faults (like bearing and motor anomalies) and to identify manufacturing processes from design data. Other work looks at how manufacturing decisions and maintenance strategies affect environmental and economic outcomes. This research combines data-driven diagnostic tools with broader questions about making factories more efficient and environmentally sustainable.
Publication output has been modest but fairly steady over the last decade, starting from zero in the late 2010s and settling around 1-3 papers per year since 2020.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
arXiv (Cornell University) · 2026
- A large manufacturing decision model for human-centric decision-making
CIRP Annals · 2025
- Bearing anomaly detection in an air compressor using an LSTM and RNN-based machine learning model
The International Journal of Advanced Manufacturing Technology · 2024
- Bearing Anomaly Detection in an Air Compressor using an LSTM and RNN-Based Machine Learning Model
Research Square · 2024
- Perspectives on future research directions in green manufacturing for discrete products
Green Manufacturing Open · 2023
- A review of research on smart manufacturing in support of environmental sustainability
International Journal of Sustainable Manufacturing · 2023
- Environmental and economic performance of different maintenance strategies for a product subject to efficiency erosion
Journal of Cleaner Production · 2022
- A review of research on smart manufacturing in support of environmental sustainability
International Journal of Sustainable Manufacturing · 2022
- Anomaly Scoring Model for Diagnosis on Machine Condition and Health Management
2022
- Identifying manufacturability and machining processes using deep 3D convolutional networks
Journal of Manufacturing Processes · 2021
- Deep Learning Based Approach for Identifying Conventional Machining Processes from CAD Data
Procedia Manufacturing · 2020
- Development and Application of a Method for Real Time Motor Fault Detection
Procedia Manufacturing · 2020
- A digital low-dropout(DLDO) regulator with 14dB power supply rejection enhancement
2016
- Procedia Manufacturing×2
- International Journal of Sustainable Manufacturing×2
- Journal of Manufacturing Processes×1
- CIRP Annals×1
- Green Manufacturing Open×1
- Xingyu Li
Engineering · Purdue University West Lafayette
- Ragu Athinarayanan
Engineering · Purdue University West Lafayette
- Praditya Ajidarma
Engineering · Purdue University West Lafayette
- Jim Davis
Engineering · The Ohio State University
- Xiaofeng Zhao
Engineering · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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