The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Role of Ontologies for CPS Implementation in Manufacturing
Open accessAbstract
Cyber Physical Systems are an evolution of embedded systems featuring a tight combination of collaborating computational elements that control physical entities. CPSs promise a great potential of innovation in many areas including manufacturing and production. This is because we obtain a very powerful, flexible, modular infrastructure allowing easy (re) configurability and fast ramp-up of manufacturing applications by building a manufacturing system with modular mechatronic components (for machining, transportation and storage) and embedded intelligence, by integrating them into a system, through a network connection. However, when building such kind of architectures, the way to supply the needed domain knowledge to real manufacturing applications arises as a problem to solve. In fact, a CPS based architecture for manufacturing is made of smart but independent manufacturing components without any knowledge of the role they have to play together in the real world of manufacturing applications. Ontologies can supply such kind of knowledge, playing a very important role in CPS for manufacturing. The paper deals with this intriguing theme, also presenting an implementation of this approach in a research project for the open automation of manufacturing systems, in which the power of CPS is complemented by the support of an ontology of the manufacturing domain.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Digital twins support supply chain management through data monitoring.
“supporting the supply chain management through data monitoring [23,26]”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [23]
The Introduction describes data capture by interconnected manufacturing equipment, and the Application example describes real-time monitoring of manufacturing-system status. Neither passage addresses digital twins supporting supply chain management. Searches for digital twins, supply chain management, and data monitoring found no passage establishing that claim. (LLM, conf=0.75)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A translation approach to portable ontology specifications
Gruber, T. (1993) · Knowledge Acquisition · doi:10.1006/knac.1993.1008 · Cited by 13,568
Cyber Physical Systems: Design Challenges
Lee, E. A. (2008) · IEEE International Symposium on Real-Time Distributed Computing (ISORC) · doi:10.1109/isorc.2008.25 · Cited by 3,638
Wolf, W. H. (2009) · Computer · doi:10.1109/mc.2009.81 · Cited by 377
Semantic Web Services in Factory Automation: Fundamental Insights and Research Roadmap
Lastra, J. L. M. (2006) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2005.862144 · Cited by 209
Object-Oriented Modeling and Design with UML
Blaha, M. R., & Rumbaugh, J. E. (1991) · Cited by 191
Cai, M., Zhang, W. Y., & Zhang, K. (2010) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2010.2076395 · Cited by 86
Developing manufacturing ontologies for knowledge reuse in distributed manufacturing environment
Lin, L., Zhang, W. Y., Lou, Y. C., Chu, C., & Cai, M. (2010) · International Journal of Production Research · doi:10.1080/00207540903349021 · Cited by 80
Semantic service discovery and orchestration for manufacturing processes
Loskyll, M., Schlick, J., Hodek, S., Ollinger, L., Gerber, T., & Pîrvu, B.-C. (2011) · doi:10.1109/etfa.2011.6058988 · Cited by 57
Semantic technologies: latest advances in agent-based manufacturing control systems
Vrba, P., Radakovič, M., Obitko, M., & Mařı́k, V. (2010) · International Journal of Production Research · doi:10.1080/00207543.2010.518746 · Cited by 45
Colledani, M., Walter Terkaj, A., Tolio, T. A. M., & Tomasella, M. (2008) · Springer eBooks · doi:10.1007/978-3-540-78431-9_15 · Cited by 44
P-PSO ontology for manufacturing systems
Garetti, M., & Fumagalli, L. (2012) · IFAC Proceedings Volumes · doi:10.3182/20120523-3-ro-2023.00222 · Cited by 40
Self-citationAn ontology-based interoperability framework for distributed manufacturing control
Diep, D., Alexakos, C., & Wagner, T. (2007) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/efta.2007.4416869 · Cited by 26
Open automation of manufacturing systems through integration of ontology and web services
Garetti, M., Fumagalli, L., Lobov, A., & Lastra, J. L. M. (2013) · IFAC Proceedings Volumes · doi:10.3182/20130619-3-ru-3018.00169 · Cited by 24
Self-citationObject-oriented representation of manufacturing systems: State of the art and perspectives
Bartolotta, A., & Garetti, M. (1998) · Springer eBooks · doi:10.1007/978-0-387-35304-3_18 · Cited by 4
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A Review of the Roles of Digital Twin in CPS-based Production Systems
Negri, E., Fumagalli, L., & Macchi, M. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.198 · Cited by 1,669
Self-citationReview of digital twin applications in manufacturing
Cimino, C., Negri, E., & Fumagalli, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.103130 · Cited by 874
Self-citationTechnologies for digital twin applications in construction
Tuhaise, V. V., Tah, J. H. M., & Abanda, F. H. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104931 · Cited by 350
Omrany, H., Al-Obaidi, K. M., Husain, A., & Ghaffarianhoseini, A. (2023) · Sustainability · doi:10.3390/su151410908 · Cited by 301
Exploring the role of digital twin for asset lifecycle management
Macchi, M., Roda, I., Negri, E., & Fumagalli, L. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.415 · Cited by 216
Self-citationA review on the characteristics of cyber-physical systems for the future smart factories
Napoleone, A., Macchi, M., & Pozzetti, A. (2020) · Journal of manufacturing systems · doi:10.1016/j.jmsy.2020.01.007 · Cited by 167
Digital Twins: Review and Challenges
Juarez, M. G., Botti, V. J., & Giret, A. S. (2021) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4050244 · Cited by 187
Trappey, A. J. C., Trappey, C. V., Govindarajan, U. H., Sun, J. J. H., & Chuang, A. C. (2016) · IEEE Access · doi:10.1109/access.2016.2619360 · Cited by 176
An open source approach to the design and implementation of Digital Twins for Smart Manufacturing
Damjanović, V., & Behrendt, W. (2019) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2019.1599436 · Cited by 165
O’Donovan, P., Gallagher, C., Leahy, K., & O’Sullivan, D. T. J. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.04.016 · Cited by 131
Requirements and languages for the semantic representation of manufacturing systems
Negri, E., Fumagalli, L., Garetti, M., & Tanca, L. (2016) · Computers in Industry · doi:10.1016/j.compind.2015.10.009 · Cited by 93
Self-citationFMU-supported simulation for CPS Digital Twin
Negri, E., Fumagalli, L., Cimino, C., & Macchi, M. (2019) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.12.033 · Cited by 89
Self-citationMaritime 4.0 – Opportunities in Digitalization and Advanced Manufacturing for Vessel Development
Sullivan, B. P., Desai, S., Rebull, J. S., Rossi, M., Ramundo, L., & Terzi, S. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.02.078 · Cited by 83
A Digital Twin-based scheduling framework including Equipment Health Index and Genetic Algorithms
Negri, E., Ardakani, H. D., Cattaneo, L., Singh, J., Macchi, M., & Lee, J. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.10.024 · Cited by 63
Self-citationA framework to manage reconfigurability in manufacturing
Napoleone, A., Pozzetti, A., & Macchi, M. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1437286 · Cited by 54
A Review of the Roles of Digital Twin in CPS-Based Production Systems
Negri, E., Fumagalli, L., & Macchi, M. (2019) · Springer eBooks · doi:10.1007/978-3-030-20704-5_13 · Cited by 46
Self-citationSurvey on ontology-based explainable AI in manufacturing
Naqvi, M. R., Elmhadhbi, L., Sarkar, A., Archimède, B., & Karray, M. H. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02304-z · Cited by 45
Modelling internal logistics systems through ontologies
Negri, E., Perotti, S., Fumagalli, L., Marchet, G., & Garetti, M. (2017) · Computers in Industry · doi:10.1016/j.compind.2017.03.004 · Cited by 40
Self-citationNapoleone, A., Negri, E., Macchi, M., & Pozzetti, A. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2074323 · Cited by 37
Self-citationMohammed, W. M., Ferrer, B. R., Iarovyi, S., Negri, E., Fumagalli, L., Lobov, A., & Lastra, J. L. M. (2017) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2017.1407874 · Cited by 36
Self-citationRojek, I., Macko, M., Mikołajewski, D., Sága, M., & Burczyński, T. (2021) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.24425/bpasts.2021.136717 · Cited by 35
Lean Thinking in the Digital Era
Cattaneo, L., Rossi, M., Negri, E., Powell, D. J., & Terzi, S. (2017) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-72905-3_33 · Cited by 33
Self-citationEmerging Technologies of Industry 4.0: Challenges and Opportunities
Okwu, M. O., Tartibu, L. K., Maware, C., Enarevba, D. R., Afenogho, J. O., & Essien, A. E. (2022) · International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) · doi:10.1109/icabcd54961.2022.9856002 · Cited by 31
Ontology-Driven Guidelines for Architecting Digital Twins in Factory Automation Applications
Mohammed, W. M., Haber, R. E., & Lastra, J. L. M. (2022) · Machines · doi:10.3390/machines10100861 · Cited by 24
Formal ontologies in manufacturing
Sanfilippo, E. M., Kitamura, Y., & Young, R. I. M. (2019) · Applied Ontology · doi:10.3233/ao-190209 · Cited by 25
Industry 4.0 and Supply Chain Management
Dhamija, P., Bedi, M., & Gupta, M. L. (2020) · International Journal of Business Analytics · doi:10.4018/ijban.2020010101 · Cited by 25
Core Characteristics of Reconfigurability and their Influencing Elements
Napoleone, A., Pozzetti, A., & Macchi, M. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.244 · Cited by 23
Application of Industry 4.0 to the Product Development Process in Project-Type Production
Urban, W., Łukaszewicz, K., & Krawczyk-Dembicka, E. (2020) · Energies · doi:10.3390/en13215553 · Cited by 25
Mechatronics: A Study on Its Scientific Constitution and Association with Innovative Products
Faria, A. C. C., & Barbalho, S. C. M. (2023) · Applied System Innovation · doi:10.3390/asi6040072 · Cited by 22
Open Interfaces for Connecting Automated Guided Vehicles to a Fleet Management System
Quadrini, W., Negri, E., & Fumagalli, L. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.02.055 · Cited by 18
Self-citation(2016) · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · Cited by 18
Self-citationCattaneo, L., Polenghi, A., & Macchi, M. (2021) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.1885062 · Cited by 17
Industry 4.0 solutions supporting Circular Economy
Rocca, R., Rosa, P., Sassanelli, C., Fumagalli, L., & Terzi, S. (2020) · International Conference on Engineering, Technology and Innovation · doi:10.1109/ice/itmc49519.2020.9198517 · Cited by 6
Self-citationSullivan, B. P., Arias–Nava, E. H., Desai, S., Rebull, J. S., Rossi, M., Ramundo, L., & Terzi, S. (2021) · IET Collaborative Intelligent Manufacturing · doi:10.1049/cim2.12012 · Cited by 13
An architecture for implementing private local automation clouds built by CPS
Ferrer, B. R., & Lastra, J. L. M. (2017) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2017.8216937 · Cited by 13
Architecture for Data Acquisition in Research and Teaching Laboratories
Quadrini, W., Galparoli, S., Nucera, D. D., Fumagalli, L., & Negri, E. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.01.333 · Cited by 12
Self-citationPolenghi, A., Roda, I., Macchi, M., & Pozzetti, A. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.08.192 · Cited by 11
An ontology-based framework for virtual enterprise integration and interoperability
Abadi, A., Ben-Azza, H., Sekkat, S., & Zemmouri, E. M. (2016) · International Conference on Electrical and Information Technologies · doi:10.1109/eitech.2016.7519607 · Cited by 11
A Semantic Model to Express Process Parameters and their Interdependencies in Manufacturing
Jeleniewski, T., Nabizada, H., Reif, J., Köcher, A., & Fay, A. (2023) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie51358.2023.10228021 · Cited by 10
Xiao, J., Anwer, N., Huang, H., Bonnard, R., Eynard, B., Huang, C., & Pei, E. (2024) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2024.2387768 · Cited by 9
Distributed control via modularized CPS architecture Lessons learnt from an industrial case study
Fumagalli, L., Negri, E., Severa, O., Balda, P., & Rondi, E. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.417 · Cited by 8
Self-citationIndustry 4.0 and Supply Chain Management
Dhamija, P., Bedi, M., & Gupta, M. L. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-8548-1.ch011 · Cited by 6
Huang, H., Xu, X. W., & Ji, T. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02634-0 · Cited by 6
Abadi, M., Abadi, C., Abadi, A., & Ben-Azza, H. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130248 · Cited by 6
Towards Ontology Enabled Agent-Based Twinning for Cyber-Physical Systems
Marah, H., Lima, L., Challenger, M., & Vangheluwe, H. L. (2023) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) · doi:10.1109/models-c59198.2023.00078 · Cited by 6
Challenges of Digital Twin System
Monsone, C. R., & Jósvai, J. (2019) · Acta Technica Jaurinensis · doi:10.14513/actatechjaur.v12.n3.514 · Cited by 6
Synchro-push: A new production control paradigm
Garetti, M., Macchi, M., Pozzetti, A., Fumagalli, L., & Negri, E. (2016) · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · Cited by 5
Self-citationService-Oriented Architecture for Intelligent Management with Data Analytics and Visualization
Lin, H., Chiu, Y.-H., Liao, W., & Chang, R.-I. (2019) · IEEE International Conference on Service-Oriented Computing and Applications (SOCA) · doi:10.1109/soca.2019.00018 · Cited by 6
Towards a Multidimensional Ontology Model for DIH-Based Organisations
Feltus, C., Nicolas, D., & Khadraoui, D. (2023) · International Journal of Knowledge and Systems Science · doi:10.4018/ijkss.326764 · Cited by 4
Płaczek, E. (2018) · Przedsiebiorczosc i Zarzadzanie · Cited by 3
MULTIDIMENSIONAL DIH4CPS ONTOLOGY
Nicolas, D., & Feltus, C. (2023) · doi:10.33965/es_ml2023_202302l002 · Cited by 2
Zakoldaev, D. A., Gurjanov, A. V., Shukalov, A. V., & Zharinov, I. O. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/537/3/032009 · Cited by 2
The encapsulation technology of CPS node sensor based on UM-BUS
Sun, L., Zhou, J., & Zhang, W. (2020) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds51347.2020.00035 · Cited by 2
An Ontology-Based Framework for Virtual Enterprise Integration and Interoperability
Abadi, A., Ben-Azza, H., Sekkat, S., & Moukhtar, Z. E. (2016) · Journal of Communication and Computer · doi:10.17265/1548-7709/2016.04.004 · Cited by 2
Ontologie-basiertes WBS für die Arbeitsplanung
Rehage, G. (2019) · Intelligente Technische Systeme, Lösungen aus dem Spitzencluster it’s OWL · doi:10.1007/978-3-662-58020-2_3 · Cited by 1
MULTIDIMENSIONAL DIH4CPS ONTOLOGY
Nicolas, D., & Feltus, C. (2023) · doi:10.33965/es_ml2023_202301l002 · Cited by 1
INTERNET OF THINGS IN THE SYSTEM OF LOGISTICS COMPANY IMPLEMENTATION CONCEPT
Pizoń, J. (2015) · Systemy Logistyczne Wojsk · doi:10.5604/01.3001.0012.7175
Woźniak, J., Budzik, G., & Zimon, D. (2018) · CeON Repository (Centre for Evaluation in Education and Science)
Ferrer, B. R., Mohammed, W. M., Jose, L., Lastra, J. L. M., & Strzelczak, S. (2019) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2019.8927430
Koncepcja wdrożenia technologii "internetu rzeczy" w systemie logistycznym przedsiębiorstwa
Pizoń, J. (2015) · Systemy Logistyczne Wojsk
Macêdo, N. F., Bezerra, F. M., de Figueiredo, F. J. S., Tavares, F. R. M., SILVA, A. L. A., & Barbosa, J. B. M. (2019) · doi:10.5935/978-85-7042-038-1.cap.07
Krommuang, A., & Krommuang, A. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0131254
Multi-view design for cyber-physical systems
Zhao, H. (2022) · theses.fr (ABES) · doi:10.70675/e2b69d5az2c42z431fz92f9z5956e9a30b76 · Cited by 7
Metadata
- Authors
- Garetti, M., Fumagalli, L., & Negri, E.
- Affiliations
- Politecnico di Milano (IT)
- Corresponding author
- Garetti, Marco
- Venue
- Management and Production Engineering Review
- Publisher
- Walter de Gruyter GmbH
- Published
- 2015-12-01
- Volume
- 6
- Issue
- 4
- Pages
- 26-32
- ISSN
- 2080-8208 (ISSN-L)
- 2082-1344
- OpenAlex
- W2416085977
- Cited by
- 64
- References
- 14
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 8 · 2022: 6 · 2021: 7 · 2020: 7 · 2019: 13 · 2018: 6 · 2017: 5 · 2016: 5 · 2015: 3
- FWCI
- 11.11
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Management and Production Engineering Review (publishedVersion, cc-by-nc-nd)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- cyber-physical systems, ontology (information science), manufacturing systems, domain knowledge representation, embedded systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
