Sangdo No
Sungkyunkwan University · Engineering
About the Lab
Professor Sangdo No's research lab specializes in smart manufacturing and industrial innovation, focusing on digital twin technologies, cyber-physical systems, and sustainable industrial platforms. The lab develops integrated frameworks that combine digital twins with artificial intelligence and the Industrial Internet of Things (IIoT) to enable resilient, adaptive, and energy-efficient manufacturing systems. Key research directions include personalized and distributed production, modular micro smart factories, and service-oriented platforms for SMEs in energy-intensive industries.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Recently, manufacturing concepts, such as personalized production and distributed manufacturing, have attracted attention owing to the ongoing revolution in industrial technology. Connected micro smart factories in factory-as-a-service system with these new manufacturing paradigms and Industrial Internet of Things (IIoT) are inefficient in terms of cost and production. To solve these problems, a digital twin, which uses a digital representation of a process, with the same configuration of manufa
Personalised production allows the supply chain (SC) to exist in various dynamic fluctuations within a make-to-order (MTO) environment. An SC for personalised production has redundant inventory and operation capacity; therefore, it requires a system that can achieve recoverability for operation resilience. Thus, a standalone cyber physical system (CPS) has limitation for SC control with MTO. To solve this problem, the CPS must be coordinated, and a systematic approach is required. This study pro
ABSTRACT In the era of the Fourth Industrial Revolution, there is a growing focus on digital twin (DT) in order to advance toward smart manufacturing. Thus, researchers have conducted numerous studies on DT and extensively developed related technologies. There are many studies that apply and analyse DT to actual manufacturing sites for the realization of a smart factory, but it is necessary to clearly consider which part of DT is applied and what function it performs in manufacturing. As such, t
To achieve efficient personalized production at an affordable cost, a modular manufacturing system (MMS) can be utilized. MMS enables restructuring of its configuration to accommodate product changes and is thus an efficient solution to reduce the costs involved in personalized production. A micro smart factory (MSF) is an MMS with heterogeneous production processes to enable personalized production. Similar to MMS, MSF also enables the restructuring of production configuration; additionally, it
Ensuring sustainability is a primary concern of the manufacturing industry. Not only does the enhancement of process and systematic efficiency secure sustainability through increased energy efficiency but it also improves the efficiency in terms of overall productivity. Dyeing and finishing industries consume massive amounts of energy and have large energy-related expenditures. The industry comprises small- and medium-sized enterprises, which have insufficient capital to pay for the energy-effic
Abstract The integration of artificial intelligence (AI) with digital twin (DT) technology has revolutionised the industry by enabling the creation of autonomous, adaptive, and resilient systems that are beyond static digital replicas. AI-enhanced DTs facilitate real-time monitoring, predictive maintenance, proactive decision making, and operational efficiency, aligning with the human-centric objectives of Industry 5.0. In this study, an AI–DT Integration framework is introduced, AI is systemati
PLM (product lifecycle management) is an innovative manufacturing paradigm which allows company's engineering contents to be developed and integrated with all business processes in the extended enterprise throughout the product lifecycle. This allows engineering decisions to be made with a full understanding of the product and its portfolio, including processes, resources, and plants. For today's manufacturing industries, support from software systems is essential for the creation, management, a
Today, megatrends such as individualization, climate change, emissions, energy, and resource scarcity, urbanization, and human well-being, impact almost every aspect of people’s lives. Transformative impacts on many sectors are inevitable, and manufacturing is not an exception. Many studies have investigated solutions that focus on diverse directions, with urban production being the focus of many research efforts and recent studies concentrating on Industry 4.0 and smart manufacturing technologi
Research Areas
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