Review
Computer Science, Interdisciplinary Applications
Yue Yin, Pai Zheng, Chengxi Li, Lihui Wang
Summary: The combination of AR and DT has attracted growing research interest in academia and industry, especially in the context of the human-centric trend. AR has the potential to integrate operators into the new generation of HCPS, with DT as a pillar component.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Review
Engineering, Manufacturing
Till Bottjer, Daniella Tola, Fatemeh Kakavandi, Christian R. Wewer, Devarajan Ramanujan, Claudio Gomes, Peter G. Larsen, Alexandros Iosifidis
Summary: In recent years, there has been a growing hype around Digital Twins (DTs) in both industry and academia. DTs have the potential to increase automation and advance towards Smart Manufacturing. This literature review focuses on DTs at the unit level in manufacturing, specifically in terms of real-time control. The review summarizes the current implementation and operation of DTs, and highlights their potential benefits in four categories: generic reference models, services, DT content (models and data), and DT deployment (hardware and software).
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2023)
Article
Chemistry, Analytical
Jung-Sing Jwo, Cheng-Hsiung Lee, Ching-Sheng Lin
Summary: Introducing Industry 4.0 into the manufacturing processes of aircraft composite materials is inevitable due to the complexity of the aerospace and defense industry. This study proposes the concept of Data Twin to simplify high-fidelity virtual models and uses machine learning approaches to achieve it. A microservice software architecture, Cyber-Physical Factory (CPF), is also proposed to simulate the shop floor environment, along with two war rooms for establishing a collaborative platform.
Article
Computer Science, Interdisciplinary Applications
Alvaro Garcia, Anibal Bregon, Miguel A. Martinez-Prieto
Summary: The evolution of digital twin has provided smart manufacturing systems with new models of collaboration. This paper aims to explore the learning opportunities offered by emerging digital twin ecosystems in manufacturing and proposes a definition and architecture for the Digital Twin Learning Ecosystem. The role of the Learning Factory concept in bridging academia and industry is highlighted.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Review
Computer Science, Interdisciplinary Applications
Concetta Semeraro, Mario Lezoche, Herve Panetto, Michele Dassisti
Summary: Manufacturing enterprises are facing the challenge of aligning themselves with new information technologies (IT) and responding to variable market demand. The key enabler of the IT revolution towards Smart Manufacturing is the digital twin (DT), which constantly synchronizes a virtual image with the real operating scenario to provide sound information for making decisions. This study aims to provide an overview of the main components of DT, their features, and interaction problems, while tracing ongoing research and technical challenges in conceiving and building DTs in different application domains and related technologies.
COMPUTERS IN INDUSTRY
(2021)
Article
Computer Science, Artificial Intelligence
Shimin Liu, Pai Zheng, Jinsong Bao
Summary: This paper analyzes the definition, characteristics and operational mechanism of Digital Twin-based manufacturing system (DTMS), and proposes a reference model for DTMS. Furthermore, potential research directions of DTMS in terms of reusability, interpretability and adaptability are highlighted.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Engineering, Industrial
Haoqi Wang, Lindong Lv, Xupeng Li, Hao Li, Jiewu Leng, Yuyan Zhang, Vincent Thomson, Gen Liu, Xiaoyu Wen, Chunya Sun, Guofu Luo
Summary: Safety management is crucial for human-centered manufacturing in Industry 5.0, but there are three challenges to bridge the gap between current workshop safety management and the desired requirements. To address these challenges, a reasoning approach using Digital Twin is proposed, which includes a machine-readable semantic reasoning framework, modeling of unsafe states ontology, and the construction of a high-fidelity virtual Digital Twin Workshop. The approach is validated through an experiment and shows promising results.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Cunbo Zhuang, Tian Miao, Jianhua Liu, Hui Xiong
Summary: Digital twin (DT) technology provides a novel, feasible, and clear implementation path for smart manufacturing and cyber-physical systems (CPS). DT is applied to various stages of the product lifecycle, including design, production, and service, with shop-floor digital twin (SDT) serving as a digital mapping model of the physical shop-floor. Challenges exist in building and applying SDT, particularly in the production phase.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Automation & Control Systems
Emre Yildiz, Charles Moller, Arne Bilberg
Summary: Smart manufacturing, driven by the 4th industrial revolution and forces like innovation, competition, and changing demands, requires manufacturing companies to reform and regenerate their product, process, and system models to stay competitive. The digital twin-based virtual factory concept shows potential in supporting manufacturing organizations to adapt to dynamic and complex environments through virtual collaboration and prototyping.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Engineering, Industrial
Zexuan Zhu, Xiaolin Xi, Xun Xu, Yonglin Cai
Summary: This paper introduces a Digital Twin-driven thin-walled part manufacturing framework, which utilizes Digital Twin technology to improve the efficiency of thin-walled parts machining and manage trial machining processes in real-time through interactive digital data.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jiwei Zhang, Haoliang Cui, Andy L. Yang, Feng Gu, Chengjie Shi, Wen Zhang, Shaozhang Niu
Summary: This paper proposes an Intelligent Digital Twin System (IDTS) based on artificial intelligence and digital twins for the paper industry. The system includes prediction models for various industry processes such as stirring speed, water consumption, air pressure, and exhaust air temperature. By collecting data and analyzing important indicators, the IDTS improves energy utilization and production efficiency, resulting in cost savings. Its effectiveness has been demonstrated in an actual paper manufacturing factory by improving operational efficiency and saving labor and maintenance costs.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Charles Ming Zheng, Lu Zhang, Yaw-Hong Kang, Youji Zhan, Yongchao Xu
Summary: This paper proposes a digital twin-driven intelligent algorithm for monitoring in-process milling parameters. The algorithm extracts milling parameters using force sensor and achieves a balance between identification accuracy and calculation efficiency. The proposed algorithm is validated through milling experiments.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Engineering, Industrial
Yepeng Fan, Jianzhong Yang, Jihong Chen, Pengcheng Hu, Xiaoyu Wang, Jianchun Xu, Bin Zhou
Summary: The new generation of industrial 4.0 intelligent manufacturing system incorporates Human-Cyber-Physical System (HCPS) to integrate human, cyber, and physical systems. This paper presents a general architecture of digital-twin visualization for flexible manufacturing systems (FMS), addressing human-machine interaction problems and proposing a digital-twin modeling concept. The study discusses visualization methods for high-value information related to lifecycle stages and presents a digital-twin modeling concept of GHOST for developing virtual scenes.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Ziqi Huang, Marcel Fey, Chao Liu, Ege Beysel, Xun Xu, Christian Brecher
Summary: Digital twin (DT) and artificial intelligence (AI) technologies are essential for achieving sustainable resilient manufacturing in Industry 4.0. A novel modeling framework is proposed in this article to address the limitations of digital twins at the shopfloor level. The framework integrates AI techniques and machine tool expertise using aggregated data, contextualizes metadata sources from different stages of production, and incorporates prior knowledge to enhance modeling reliability in dynamic industrial circumstances. A hybrid learning-based digital twin is developed and tested, which enables learning uncertainties in real industrial environments and improves modeling reliability based on data quality and accessibility.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Article
Chemistry, Analytical
Sangsu Choi, Jungyub Woo, Jun Kim, Ju Yeon Lee
Summary: A digital twin is a key technology in the fourth industry that allows analysis of data and monitoring of systems by combining virtual and physical models. This paper presents a digital twin system based on an interoperable data model, explaining how to build it using edge devices, data analytics, and 3D visualization. The system enables continuous collaboration between field engineers, designers, and layout engineers, and can be used by both large companies and small and medium-sized enterprises.
Article
Computer Science, Interdisciplinary Applications
Martin Manns, Klaus Fischer, Han Du, Philip Slusallek, Kosmas Alexopoulos
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2018)
Article
Computer Science, Information Systems
Elaheh Maleki, Farouk Belkadi, Nikoletta Boli, Berend Jan van der Zwaag, Kosmas Alexopoulos, Spyridon Koukas, Mihai Marin-Perianu, Alain Bernard, Dimitris Mourtzis
IEEE INTERNET OF THINGS JOURNAL
(2018)
Article
Computer Science, Interdisciplinary Applications
Kosmas Alexopoulos, Konstantinos Sipsas, Evangelos Xanthakis, Sotiris Makris, Dimitris Mourtzis
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2018)
Article
Computer Science, Interdisciplinary Applications
Nikolaos Nikolakis, Kosmas Alexopoulos, Evangelos Xanthakis, George Chryssolouris
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2019)
Article
Engineering, Manufacturing
Farouk Belkadi, Nikoletta Boli, Luis Usatorre, Elaheh Maleki, Kosmas Alexopoulos, Alain Bernard, Dimitris Mourtzis
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2020)
Article
Chemistry, Multidisciplinary
Ioannis Anagiannis, Nikolaos Nikolakis, Kosmas Alexopoulos
APPLIED SCIENCES-BASEL
(2020)
Article
Chemistry, Analytical
Xanthi Bampoula, Georgios Siaterlis, Nikolaos Nikolakis, Kosmas Alexopoulos
Summary: This study investigates the use of machine learning algorithms to improve maintenance of industrial equipment through a transition from preventive to predictive maintenance using deep learning algorithms. An autoencoder-based methodology is employed for classifying real-world data to estimate the remaining useful life of monitored equipment.
Article
Chemistry, Multidisciplinary
Kosmas Alexopoulos, Nikolaos Nikolakis, Evangelos Xanthakis
Summary: This work presents an approach for the digital transformation of a manufacturing SME in the mold production industry. It focuses on improving planning and monitoring capabilities by transitioning from manual practices to digital and smart manufacturing configurations. The digital solution proposed includes technologies such as the Internet of Things and data management. The study demonstrates the benefits of digitalization in reducing daily production management tasks and easing the burden of planning and monitoring.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Vasilis Siatras, Emmanouil Bakopoulos, Panagiotis Mavrothalassitis, Nikolaos Nikolakis, Kosmas Alexopoulos
Summary: Industry 4.0 aims to connect different industrial assets within a manufacturing environment. The Asset Administration Shell (AAS) serves as a digital representation for assets, applicable to physical and digital assets like AI agents and databases. Multi-agent systems (MASs) are useful for decentralized optimization in planning and scheduling scenarios. To address these challenges, this work proposes an AAS-based information model for scheduling agents, allowing multiple AI methods to be encapsulated within a single agent.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Manufacturing
Sotiris Makris, Kosmas Alexopoulos, George Michalos, Andreas Sardelis
JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Panagiotis Aivaliotis, Konstantinos Georgoulias, Kosmas Alexopoulos
2019 IEEE INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY AND INNOVATION (ICE/ITMC)
(2019)
Proceedings Paper
Education & Educational Research
Dimitris Mavrikios, Kosmas Alexopoulos, Konstantinos Georgoulias, Sotiris Makris, George Michalos, George Chryssolouris
RESEARCH. EXPERIENCE. EDUCATION.
(2019)
Proceedings Paper
Green & Sustainable Science & Technology
D. Mourtzis, N. Boli, K. Alexopoulos, D. Rozycki
25TH CIRP LIFE CYCLE ENGINEERING (LCE) CONFERENCE
(2018)
Proceedings Paper
Computer Science, Information Systems
Kosmas Alexopoulos, Spyros Koukas, Nikoletta Boli, Dimitris Mourtzis
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: THE PATH TO INTELLIGENT, COLLABORATIVE AND SUSTAINABLE MANUFACTURING
(2017)