Virtual Engineering Object (VEO): Toward experience-based design and manufacturing for industry 4.0
Abstract
In this article we propose the concept, its framework, and implementation methodology for Virtual Engineering Objects (VEO). A VEO is the knowledge representation of an engineering object that embodies its associated knowledge and experience. A VEO is capable of adding, storing, improving, and sharing knowledge through experience. Moreover, it is demonstrated that VEO is a specialization of a Cyber-Physical System (CPS). In this article, it is shown through test models how the concept of VEO can be implemented with the Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). The test model confirmed that the concept of VEO is able to capture and reuse the experience of engineering artifacts, which can be beneficial for efficient decision-making in industrial design and manufacturing.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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 sensors are employed in reuse in metal forming, manufacturing industry, virtual engineering objects, and CAx systems within aerospace.
“In the reuse circular approach, digital sensors are seen to be employed in metal forming [130], in the manufacturing industry [131], for virtual engineering objects [132], and for computer aided technologies (CAx) systems within the aerospace industry [133].”
From Digitisation and the Circular Economy: A Review of Current Research and Future Trends (2018) · cites this paper as [132]
Cites
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 · see citing works
Journal of Universal Computer Science
Salomaa, A. K., Calude, C. S., & Maurer, H. A. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.3217/jucs-000-00-0109 · Cited by 3,394 · see citing works
An OWL Ontology of Set of Experience Knowledge Structure
Sanín, C., Szczerbicki, E., & Toro, C. (2007) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.3217/jucs-013-02-0209 · Cited by 77 · see citing works
Self-citationSet of experience: a knowledge structure for formal decision events
Sanín, C., & Szczerbicki, E. (2005) · Foundations of Control and Managment Sciences · Cited by 63 · see citing works
Self-citationDecisional DNA: A multi-technology shareable knowledge structure for decisional experience
Sanín, C., Toro, C., Zhang, H., et al. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.08.029 · Cited by 61 · see citing works
Self-citationInformation systems architecture and technology
Świątek, J., Borzemski, L., Grzech, A., et al. (2013) · Cited by 57 · see citing works
Application of a Multi-domain Knowledge Structure: The Decisional DNA
Sanín, C., Mancilla-Amaya, L., Szczerbicki, E., & Cayfordhowell, P. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-04170-9_3 · Cited by 54 · see citing works
Self-citationSanín, C., & Szczerbicki, E. (2007) · Cybernetics & Systems · doi:10.1080/01969720701601189 · Cited by 51 · see citing works
Self-citationSet of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA): Past, Present and Future
Shafiq, S. I., Sanín, C., & Szczerbicki, E. (2014) · Cybernetics & Systems · doi:10.1080/01969722.2014.874830 · Cited by 46 · see citing works
Self-citationConsensus-based partitions in the space of ordered partitions
Daniłowicz, C., & Nguyên, N. T. (1988) · Pattern Recognition · doi:10.1016/0031-3203(88)90061-1 · Cited by 40 · see citing works
Conceptual model for semantic representation of industrial manufacturing processes
García-Crespo, Á., Ruíz‐Mezcua, B., López-Cuadrado, J. L., et al. (2010) · Computers in Industry · doi:10.1016/j.compind.2010.01.004 · Cited by 38 · see citing works
Decisional DNA and the Smart Knowledge Management System
Sanín, C., & Szczerbicki, E. (2011) · IGI Global eBooks · doi:10.4018/978-1-59904-826-0.ch006 · Cited by 37 · see citing works
Self-citationSanín, C., & Szczerbicki, E. (2006) · Cybernetics & Systems · doi:10.1080/01969720500425046 · Cited by 35 · see citing works
Self-citationConstructing and mining a semantic-based academic social network
Duong, T. H., Nguyên, N. T., & Jo, G. S. (2010) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/ifs-2010-0451 · Cited by 33 · see citing works
Cognitive understanding of knowledge processing and modeling in design
Qiu, Y. F., Chui, Y. P., & Helander, M. G. (2008) · Journal of Knowledge Management · doi:10.1108/13673270810859587 · Cited by 24 · see citing works
Shafiq, S. I., Sanín, C., Szczerbicki, E., et al. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.08.146 · Cited by 13 · see citing works
Self-citationDecisional DNA Based Framework for Representing Virtual Engineering Objects
Shafiq, S. I., Sanín, C., Szczerbicki, E., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05476-6_43 · Cited by 13 · see citing works
Self-citationToward decisional DNA: developing holistic set of experience knowledge structure
Sanín, C., & Szczerbicki, E. (2008) · NOVA (University of Newcastle Australia) · Cited by 13 · see citing works
Self-citationZhang, H., Sanín, C., & Szczerbicki, E. (2013) · Cybernetics & Systems · doi:10.1080/01969722.2013.762280 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Industry 4.0: A survey on technologies, applications and open research issues
Lu, Y. (2017) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2017.04.005 · Cited by 3,012 · see citing works
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2018.05.009 · Cited by 1,440 · see citing works
Human-centered design of work systems in the transition to industry 4.0
Kadir, B. A., & Broberg, O. (2021) · Applied Ergonomics · doi:10.1016/j.apergo.2020.103334 · Cited by 177 · see citing works
- Authors
- Shafiq, S. I., Sanin, C., Toro, C., & Szczerbicki, E.
- Affiliations
- University of Newcastle Australia (AU)
- Vicomtech (ES)
- Gdańsk University of Technology (PL)
- Corresponding author
- Shafiq, Syed Imran
- Venue
- Cybernetics and Systems
- Publisher
- Informa UK Limited
- Published
- 2015-02-17
- Volume
- 46
- Issue
- 1-2
- Pages
- 35-50
- ISSN
- 0196-9722 (ISSN-L)
- 1087-6553
- OpenAlex
- W2020623523
- Cited by
- 162
- References
- 19
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 7 · 2023: 15 · 2022: 19 · 2021: 27 · 2020: 34 · 2019: 24 · 2018: 12 · 2017: 7 · 2016: 11 · 2015: 2
- FWCI
- 18.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cybernetics & Systems (publishedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Manufacturing Process and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Computational Techniques and Applications
- Keywords
- experience-based design, knowledge representation, experiential knowledge, knowledge sharing, cyber-physical systems, Industry 4.0, decision-making, Decisional DNA, engineering artifacts
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
