|Academic Profile |
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Assoc Prof Moon Seung Ki
Associate Professor, School of Mechanical & Aerospace Engineering
|Prof Moon received his Ph.D. degree in Industrial Engineering from the Pennsylvania State University in 2008, his M.S. and B.S. degrees in Industrial Engineering from Hanyang University, South Korea, in 1995 and 1992, respectively. He worked as a Senior Research Engineer at the Hyundai Motor Company, South Korea for eight years before embarking on his PhD degree. After completing his doctoral degree, he joined the Department of Mechanical Engineering, Texas A&M University for one year as a postdoctoral research associate. Prior to joining MAE, he served as a Research Associate of Industrial & Manufacturing Engineering at the Pennsylvania State University between October 2009 and March 2011.|
|He is interested in the boundary-spanning research that integrates the knowledge of design, engineering, and economics. His current focuses include applying sciences and economic theory to the design of customized and sustainable products, services and systems, strategic and multidisciplinary design optimization, data mining, advanced modeling and simulation, design for additive manufacturing/3D printing, embedded sensor design for 3D Printing, smart factory, and redesign and remanufacturing for green technologies.|
- 3D Printing of a Thruster for Use on CubeSats
- A data driven design strategy to improve quality in additive manufacturing
- An Intelligent Decision Making Modeling and Simulation to support product Recovery
- Design and Process Optimization of Polyamide 11 (PA11) 3D Printed Structures for Enhanced Impact Performance in Automotive Applications
- Development of direct printing material and process on 3D structure for wearable devices
- Development of method for 3D printing of electronic sensors
- Energy Effective Service Design for Plug-In-Hybrid Electric Vehicles in a Smart Grid
- Intelligent Multiagent based Robust Dynamic Scheduling in Smart Factories
- Maintainable Design Appraisal System
- Metal 3D Printing in Aerospace Industry
- Novel approaches for biometric systems
- Programme: Aerospace and Defence
- Simulation Based Production Flow Design in Batch Processing Manufacturing
- Work Package 1 (Laser-aided Additive Manufacturing)
- Zhang, H., Choi, J.P., Moon, S.K., and Ngo, T.H. (2020). A hybrid multi-objective optimization of aerosol jet printing process via response surface methodology. Additive Manufacturing, 33(May), 101096.
- Sacco, E. and Moon, S.K. (2019). Additive Manufacturing for Space: Status and Promise. International Journal of Advanced Manufacturing Technology, 105(10), 4123-4146.
- Zhang, H., Moon, S.K., and Ngo, T.H. (2019). Hybrid Machine Learning Method to Determine the Optimal Process Operating Window in Aerosol Jet 3D Printing. ACS Applied Materials and Interfaces, 11(19), 17994-18003.
- Kim, S. and Moon, S.K. (2019). Eco-Modular Product Architecture Identification and Assessment for Product Recovery. Journal of Intelligent Manufacturing, 30(1), 383-403.
- Yao, X., Moon, S.K., and Bi, G. (2017). A Hybrid Machine Learning Approach for Additive Manufacturing Design Feature Recommendation. Rapid Prototyping Journal, 23(6), 983-997.