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Um Jumyoung

Kyung Hee University · Engineering

About the Lab

Professor Um Jumyoung's research lab specializes in digital transformation and intelligent manufacturing systems, focusing on the integration of advanced technologies such as augmented reality (AR), cyber-physical systems, and standardized data models (e.g., STEP-NC) to enhance industrial efficiency and decision-making. The lab explores practical applications of AR in warehousing, construction site management, and flexible manufacturing, with an emphasis on low-cost, accessible solutions for small and medium enterprises. Research also extends to energy-efficient manufacturing processes, particularly in automotive production, where AR and simulation tools support sustainable and data-driven engineering decisions.

augmented realitydigital manufacturingenergy efficiencycyber-physical systemsSTEP-NC

Research Overview

Papers
85
Total Citations
986
Papers (5y)
50
Primary Field
Engineering

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
50total
2022
2023
2024
2025
2026
Citations per year (5y)
216total
20222023202420252026

Selected Papers

15
1
Article|138 citations·2017
Augmented Reality in Warehouse Operations: Opportunities and Barriers
Marie-Hélène Stoltz, Vaggelis Giannikas, Duncan McFarlane, James W. A. Strachan, Jumyung Um, Rengarajan Srinivasan
IFAC-PapersOnLineOA

In today’s business environment, the efficiency of warehouses can be critical for the efficiency of the overall supply chains they belong to. As a result, new technologies are being tested and adopted in industry to improve the performance of warehouse operations. An example technology that has recently gained interest by both academia and industry is augmented reality. In this paper, we investigate the opportunities arising from the usage of augmented reality in warehouses as well as the barrie

Computer Vision and Pattern RecognitionComputer Science
2
Article|86 citations·2017
Plug-and-Simulate within Modular Assembly Line enabled by Digital Twins and the use of AutomationML
Jumyung Um, Stephan Weyer, Fabian Quint
IFAC-PapersOnLineOA

The Digital Age is becoming the de-facto of future direction in manufacturing domain. Its main driver is the pressure of shorter product life cycles and leads companies to faster engineering time of their production lines. To cope with the need, recent literature has successfully shown modular production and assembly line concepts by using standardized interfaces on electromechanical and communication level. This change entails new challenges. Given the fact that production life cycle decreases,

Industrial and Manufacturing EngineeringEngineering
3
Article|52 citations·2022
Low-cost mobile augmented reality service for building information modeling
Jumyung Um, Joungmin Park, Seo yeon Park, Gökçen Yılmaz
SJR Q1Automation in ConstructionOA

Informed decision-making is crucial for construction site operators. Cyber-physical systems, including various technologies such as augmented reality and building automation tools, are gathering popularity within the infrastructure management sector. However, they are expensive and inaccessible to adopt for most small organizations. This paper describes a prototype of low-cost mobile augmented reality service for BIM and demonstrated its usability for pipe maintenance by supporting inspection, w

Computer Vision and Pattern RecognitionComputer Science
4
Article|51 citations·2016
STEP-NC compliant process planning of additive manufacturing: remanufacturing
Jumyung Um, Matthieu Rauch, Jean-Yves Hascoët, Ian Stroud
SJR Q1The International Journal of Advanced Manufacturing TechnologyOA
Industrial and Manufacturing EngineeringEngineering
5
Article|34 citations·2008
An architecture design with data model for product recovery management systems
Jumyung Um, Joo-Sung Yoon, Suk‐Hwan Suh
SJR Q1Resources Conservation and Recycling
Management of Technology and InnovationBusiness, Management and Accounting
6
Article|33 citations·2023
Digital twin for autonomous collaborative robot by using synthetic data and reinforcement learning
Kyusung Kim, Min-Ho Choi, Jumyung Um
SJR Q1Robotics and Computer-Integrated Manufacturing
Control and Systems EngineeringEngineering
7
Article|23 citations·2018
Modular augmented reality platform for smart operator in production environment
Jumyung Um, Jens Popper, Martin Ruskowski

The trend of mass customization requires highly flexible production systems, which pose a new challenge for workers. Numerous concepts using augmented reality technologies have been developed to offer support in manual assembly processes. Newer AR devices are equipped with more and more functions, such as microphones, speakers or multiple camera systems, which can be used to help workers. However, wearable devices offer only limited computational power and limited flexibility of their interface

Computer Vision and Pattern RecognitionComputer Science
8
Article|21 citations·2016
STEP-NC machine tool data model and its applications
Jumyung Um, Suk‐Hwan Suh, Ian Stroud
SJR Q1International Journal of Computer Integrated Manufacturing

The machine tool data model of STEP-NC (ISO 14649) was conceived as a necessary extension to the original STEP-NC set of standards to make efficient control possible. The intention of this paper is to describe the background to the data model as well as related research work building on a higher level of information than can currently be found in the control information. The development of STEP-NC controllers promises improved manufacturing and resource use. However, even with legacy controllers

Industrial and Manufacturing EngineeringEngineering
9
Article|18 citations·2013
Total Energy Estimation Model for Remote Laser Welding Process
Jumyung Um, Ian Stroud
Procedia CIRPOA

The issues in the energy-efficiency process have much interest in the automotive industry. The energy criteria are also important in machine selection as well as productivity when new equipment is introduced on a shop floor. Remote laser welding having benefits for productivity and for energy saving is receiving attention in automotive assembly lines, but introducing this innovative equipment is a significant decision because of high initial cost in spite of the advantages. This paper specifical

Industrial and Manufacturing EngineeringEngineering
10
Article|18 citations·2023
BoxStacker: Deep Reinforcement Learning for 3D Bin Packing Problem in Virtual Environment of Logistics Systems
Shokhikha Amalana Murdivien, Jumyung Um
SJR Q1SensorsOA

Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the lack of human experts. Deep Reinforcement Learning is a potential solution to solve more complex problems by introducing artificial neural networks in Reinforcement Learning. In this paper, a game engine was used for Deep Reinforcement Learning trai

Industrial and Manufacturing EngineeringEngineering
11
Article|13 citations·2024
Deploying data analytics models in asset administration shells: Energy prediction in manufacturing
Seung‐Jun Shin, Jumyung Um
SJR Q1Engineering Applications of Artificial Intelligence
Industrial and Manufacturing EngineeringEngineering
12
Article|13 citations·2014
Factory Planning System Considering Energy-efficient Process under Cloud Manufacturing
Jumyung Um, Yong-Chan Choi, Ian Stroud
Procedia CIRPOA

Cloud computing sets to make a change in business between enterprises over the internet based on services providing dynamically reconfigurable and virtualized networks. It is difficult for an aircraft manufacturer to consider the performance and status of each factory (i.e., capacity, inventory, order, technology, etc.) because of the huge amount of suppliers. In legacy environments, the supplier consumed a long time to identify the machine and process required and the energy to be consumed in o

Industrial and Manufacturing EngineeringEngineering
13
Article|13 citations·2023
Convolutional LSTM based melt-pool prediction from images of laser tool path strategy in laser powder bed fusion for additive manufacturing
Joung Min Park, Min-Ho Choi, Jumyung Um
SJR Q1The International Journal of Advanced Manufacturing Technology
Mechanical EngineeringEngineering
14
Article|12 citations·2017
Development a Modular Factory with Modular Software Components
Jumyung Um, Klaus Fischer, Torsten Spieldenner, Dennis Kolberg
Procedia ManufacturingOA

Recent market trends require extremely short product life cycles to cope with individual customer requirements. The key technology to deal with these requirements is plug-and-produce, which reduces engineering time to change the production lines rapidly. This challenge is solved by the concept of a modular factory which allows to reconfigure individual machine stations without the need of extensive engineering effort. Current solutions focus on hardware approaches such as modular frames, cables

Industrial and Manufacturing EngineeringEngineering
15
Article|11 citations·2025
A Blockchain-Based Digital Product Passport System Providing a Federated Learning Environment for Collaboration Between Recycling Centers and Manufacturers to Enable Recycling Automation
Minji Kim, Cheol Hyeon Han, Kyung Jin Park, Jae Moon, Jumyung Um
SJR Q1SustainabilityOA

As the global emphasis on sustainable resource management intensifies, the need for efficient recycling automation becomes critical. This paper addresses the integration of blockchain technology and federated learning to enhance the automation and efficiency of recycling processes. The authors propose a novel digital product passport system that utilizes blockchain for secure data sharing and federated learning for continuous improvement of recycling automation models. Manufacturers develop base

Information SystemsComputer Science

Research Areas

Industrial and Manufacturing EngineeringComputer Vision and Pattern RecognitionControl and Systems EngineeringMechanical EngineeringBiomedical EngineeringAutomotive Engineering

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