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스마트공장을 위한 빅데이터 애널리틱스 플랫폼 아키텍쳐 개발
신승준(Seung-Jun Shin),우정엽(Jungyub Woo),서원철(Wonchul Seo) 한국멀티미디어학회 2016 멀티미디어학회논문지 Vol.19 No.8
While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.
디지털 트윈 기반 역사 공기질 통합 모니터링 및 분석 시스템
최상수(SangSu Choi),우정엽(Jungyub Woo),송인호(Inho Song) 대한기계학회 2022 大韓機械學會論文集A Vol.46 No.1
지하철 역사의 미세먼지 절감을 위한 ICT 기반의 연구들이 활발히 수행되고 있다. 본 연구에서는 디지털 트윈 기반 통합 공기질 모니터링 및 분석 시스템을 소개한다. 이 시스템은 (1) 지하철 역사에 설치된 다양한 IoT 센서로부터 데이터들을 수집하여 엣지 디바이스를 통해 디지털 트윈에 전달하고, (2)실제와 동일한 실감형 3D 디지털 트윈을 통해 현재의 상황을 모니터링하며, (3) AI와 CFD 시뮬레이션 기술을 통해 공기질의 예측을 수행하여 최적의 공기질 상황으로 역사를 유지할 수 있도록 돕는다. 이 시스템을 강남역에 적용하여 결과를 분석했다. ICT-based studies are being actively conducted to reduce fine dust in subway stations. In this study, an integrated air quality monitoring and analysis system based on a digital twin was introduced. This system provided the following functionalities: (1) collected data from various IoT sensors installed in subway stations, and delivered it to the digital twin through edge devices; (2) monitored the current situation through realistic 3D digital twin, and (3) helped to maintain the optimal air quality of subway stations through air quality prediction, using AI and CFD simulation technology. Results were analyzed by applying the system to Gangnam station.
스마트 제조를 위한 클라우드 플랫폼 기반 사용자 친화적인 디지털 트윈 구축 방법
최상수(SangSu Choi),우정엽(Jungyub Woo),박양호(Yangho Park),송인호(Inho Song) 대한기계학회 2021 大韓機械學會論文集A Vol.45 No.2
스마트 제조 시대의 도래로 선진국과 글로벌 제조 기업들을 중심으로 스마트 공장을 활발히 구축하고 있다. 디지털 트윈은 진보된 형태의 스마트 공장 구축을 가능하게 하는 미래 핵심 기술이다. 디지털 트윈 기반 스마트 공장은 (1) 실제 공장과 동일한 디지털 공장을 모델링하고, (2) 물리적 공장과 디지털 공장을 연결하며, (3) 시뮬레이션, 인공지능과 같은 분석 기술들로 예측하고, (4) VR/AR을 통해 직접 체험하며 빠른 의사결정을 통해 물리적 공장을 제어하는 절차로 구축된다. 디지털 트윈은 전문가 의존도가 매우 높은 기술이며, 구축에 많은 시간과 비용이 소요된다. 본 논문에서는 전문가뿐만 아니라 초보자들도 쉽고 빠르게 디지털 트윈을 구축이 가능한 클라우드 기반 디지털 트윈 플랫폼을 소개한다. 다양한 제조기업들의 구축 사례들에 대해 설명하고, 그 효과를 분석했다. With the advent of the smart manufacturing era, advanced countries and global manufacturing enterprises are actively building smart factories. Digital twin (DT) is the future key technology that enables the building of advanced smart factories. DT-based smart factories are built by following procedures that consist of (1) modeling a digital factory that is identical to the physical factory, (2) connecting the physical factory and digital factory, (3) predicting using analysis techniques, such as simulation and artificial intelligence, and (4) controlling the physical factory through quick decision-making based on direct experience using virtual or augmented reality. DT is a technique in which experts are heavily relied upon, and it takes much time and costs to develop DT. In this paper, we introduce a cloud-based DT platform that enables novices and experts to build DT quickly and easily. Cases applicable to various manufacturing companies were described, and the effects were analyzed.
최상수(SangSu Choi),우정엽(Jungyub Woo),김준(Jun Kim),최원화(Wonhwa Choi),김지수(Jisu Kim),이주연(Ju Yeon Lee) (사)한국CDE학회 2021 한국CDE학회 논문집 Vol.26 No.1
With the advent of the 4th industrial revolution, digital twin technology is in the spotlight. Cases of digital twin creation based on the latest information and communication technologies are being actively reported by advanced countries and global companies. However, sometimes the unclear definition of digital twin confuses readers. Kritzinger et al. classified terms and concepts into Digital Model (DM), Digital Shadow (DS), and Digital Twin (DT) depending on whether the data flow between the physical entity and the digital entity is automatic or manual. This paper examines Korean papers searched for digital twin and manufacturing keywords in Google Scholar for 3 years and analyzes them by dividing the level of building digital twin by DM, DS, and DT. Based on the results, this paper discusses the direction of digital twin creation.
Energy Prediction Modeling for Numerical Control Programs Using MTConnect
신승준(Seung-Jun Shin),우정엽(Jungyub Woo),서원철(Wonchul Seo),정여진(Yeo-Jin Jeong) Korean Society for Precision Engineering 2017 한국정밀공학회지 Vol.34 No.5
In the metal-cutting industry, energy prediction is important for environmentally-conscious manufacturing because it enables a numerical anticipation of the energy consumption from the input of the process parameters, and therefore it contributes to the increasing of the energy-efficiency of the machine-tool operations. This paper proposes an energy-prediction modeling approach for numerical-control programs based on historical machine-monitoring data that have been collected from machine-tool operations. The proposed approach can create accurate energy-prediction models that forecast the energy that is consumed by the execution of a numerical-control program. Also, it can create machine-specific energy-prediction models that accommodate the variety of shop-floor machining contexts. For this purpose, it uses MTConnect to represent the machine-monitoring data to embody an interoperable data-collection environment regarding the shop floor. This paper also presents a case study to show the feasibility and practicability of the proposed approach.