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arxiv: 2605.22457 · v1 · pith:C2BHCUILnew · submitted 2026-05-21 · 💻 cs.AI · cs.SY· eess.SY

KAPPS: A knowledge-based CPPS Architecture for the Circular Factory

Etienne Hoffmann , Jan-Felix Klein , S\"oren Weindel , Max Goebels , Sebastian Behrendt , Daniel Hern\'andez , Ratan Bahadur Thapa , J\"urgen Fleischer
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Kai Furmans Steffen Staab
This is my paper

Pith reviewed 2026-05-22 06:27 UTC · model grok-4.3

classification 💻 cs.AI cs.SYeess.SY
keywords circular manufacturingknowledge graphcyber physical production systemontologydata integrationadaptive planninguncertainty handlingarchitecture design
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The pith

KAPPS turns an ontology-grounded knowledge graph into the factory's authoritative write-time state for circular manufacturing.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Circular manufacturing must process used products that arrive with varying conditions and uncertainties, unlike the fixed sequences of traditional linear production. This requires manufacturing systems that adapt processes dynamically and combine knowledge from both human workers and machines. Standard IT architectures for factories assume stable, predictable setups and cannot track or respond to the unique state of each individual component during operation. The paper first extracts fourteen requirements from five different perspectives and then builds KAPPS around a knowledge graph that serves as the single, always-updated source of truth for the entire factory.

Core claim

The central claim is that KAPPS provides a workable architecture for circular factories by placing an ontology-grounded knowledge graph at the center as the unifying data backbone. A semantic interface layer ensures data and information move consistently across different machines and services, support reasoning, and allow communication. This change makes the knowledge graph the factory's authoritative record that gets written to and read from in real time. Additional modules handle rule enforcement and event-driven planning so that execution plans can adjust incrementally when conditions are uncertain and when human and machine knowledge must be combined.

What carries the argument

The ontology-grounded knowledge graph together with a semantic interface layer, which acts as the single unifying backbone and turns into the factory's authoritative write-time state.

If this is right

  • Execution plans can adapt incrementally when product conditions are uncertain.
  • Human operators and automated systems can exchange knowledge through the shared graph.
  • Anomaly detection becomes possible by querying services mediated by the knowledge graph.
  • Runtime constraints can be enforced directly in modular conveyor or assembly setups.
  • The full set of fourteen requirements for circular production systems is addressed.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could support material traceability across entire recycling networks by storing component histories in one queryable structure.
  • Connecting multiple KAPPS instances might allow factories to share optimization knowledge about common product types.
  • Scalability tests on graph update rates would be required before applying the system to high-throughput re-manufacturing lines.
  • Linking the graph to regulatory reporting tools could simplify compliance checks for circular economy rules.

Load-bearing premise

The fourteen requirements drawn from five perspectives are necessary and sufficient, and an ontology-grounded knowledge graph can represent each unique component's state at runtime without unacceptable delays or complexity.

What would settle it

Running the two demonstrated use cases at scale with highly variable reused products and measuring whether the knowledge graph updates introduce latency that breaks real-time constraint enforcement or anomaly detection would show if the architecture works as claimed.

Figures

Figures reproduced from arXiv: 2605.22457 by Daniel Hern\'andez, Etienne Hoffmann, Jan-Felix Klein, J\"urgen Fleischer, Kai Furmans, Max Goebels, Ratan Bahadur Thapa, Sebastian Behrendt, S\"oren Weindel, Steffen Staab.

Figure 1
Figure 1. Figure 1: The DSRM Process adopted from [10] for the development of the KAPPS Architecture. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Ontology layer: A core ontology defines system-wide con￾cepts shared across the Circular Factory; a service ontology and mul￾tiple domain ontologies extend this core to cover deployment-specific concepts, each maintained as an independent module. External vocab￾ularies from Linked Open Data are referenced where reuse applies. Ontology development is managed under Git-based version control, with a CI/CD pip… view at source ↗
Figure 3
Figure 3. Figure 3: Interface and service layer in operation.Two middleware instances coordinate through the graph database: the left instance wraps a Transformer Cell and bridges industrial hardware via an OPC UA connector; the right instance wraps a pure software service (here, a disassembly planner). Both register their workflows as typed ser￾vice individuals in the graph at startup, exchange data through the OGM (fetch & … view at source ↗
Figure 4
Figure 4. Figure 4: KAPPS architecture overview. The four layers introduced in Sections 4.1 – 4.4 are shown in their integration: the Ontology Layer governs the vocabulary and constraints, persisted into the Knowledge Base Layer through CI/CD pipelines; the Service Layer hosts software services that interact with the graph through OGM-mediated commits; and the Interface Layer connects the upper three layers to physical resour… view at source ↗
Figure 5
Figure 5. Figure 5: Physical setup of the robotic transformer cell. The screwing [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Closed-loop architecture of UC1. The knowl [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Minimal domain ontology for the anomaly detection -learning loop. Because the KAPPS infrastructure absorbs OGM binding and [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The FlexConveyor System [64] serving as use case example. From left to right: system overview, the conveying unit allowing conveyance [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
read the original abstract

While linear manufacturing relies on homogeneous materials and predefined process sequences, circular manufacturing reintroduces used products with heterogeneous and uncertain conditions. This shift demands manufacturing systems capable of handling variable product states, dynamically reconfigurable processes, and the integration of human and machine knowledge. Conventional manufacturing IT architectures, designed for stable structures and deterministic execution, are unable to meet these requirements, as they cannot adequately represent and manage the uniqueness of individual components at runtime. Following a design science methodology for developing a Cyber Physical Production System for circular manufacturing, we derive 14 requirements from five complementary perspectives. Based on these requirements, we design KAPPS, a knowledge-based architecture that uses an ontology-grounded knowledge graph as a unifying data backbone, combined with a semantic interface layer to enable consistent data and information integration, reasoning, and communication across heterogeneous systems and services, turning the knowledge graph from an integration layer into the factories authoritative write-time state. KAPPS incorporates modules for constraint enforcement and event-driven planning, enabling incremental adaptation of execution plans under uncertainty and human-machine knowledge exchange. The applicability of KAPPS is demonstrated through two implemented use cases: (i) Anomaly detection and learning through knowledge graph mediated services and (ii) runtime constraint enforcement in a modular conveyor system. Subsequently, the architecture is evaluated against the 14 requirements (ed. abstract shortened)

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims to develop KAPPS, a knowledge-based CPPS architecture for circular manufacturing. Following a design science methodology, it derives 14 requirements from five complementary perspectives, proposes an architecture that uses an ontology-grounded knowledge graph as a unifying data backbone and the factory's authoritative write-time state, incorporates modules for constraint enforcement and event-driven planning to enable incremental adaptation under uncertainty, demonstrates applicability via two implemented use cases (anomaly detection through mediated services and runtime constraint enforcement on a modular conveyor system), and evaluates the architecture against the 14 requirements.

Significance. If validated, the work could advance CPPS design for circular manufacturing by providing a systematic way to integrate heterogeneous and uncertain product states through a knowledge-centric backbone that supports reasoning and human-machine exchange. The explicit derivation of requirements from multiple perspectives and the positioning of the knowledge graph as more than an integration layer are constructive contributions to handling variability in remanufacturing contexts.

major comments (2)
  1. [Abstract and architecture design] Abstract and architecture description: the central claim that the ontology-grounded knowledge graph functions as the 'factories authoritative write-time state' enabling consistent execution decisions is load-bearing, yet the manuscript provides no mechanisms for concurrency control, conflict resolution under heterogeneous updates, or bounds on reasoning latency; without these, the single-source-of-truth property cannot be assessed for real-time circular manufacturing scenarios where component states are unique and uncertain.
  2. [Use cases] Use-case demonstrations: the two implemented use cases show basic integration and reasoning but report no quantitative metrics (e.g., update latencies, reasoning overhead, or consistency error rates) nor comparisons against alternative architectures or baselines, leaving the claim of practical applicability for dynamic plan adaptation unsupported at the level required for the central contribution.
minor comments (2)
  1. [Evaluation] The evaluation section would be strengthened by an explicit table or mapping that links each of the 14 requirements to specific KAPPS components or modules.
  2. [Architecture] Clarify the precise scope and interfaces of the 'semantic interface layer' early in the architecture section to avoid ambiguity when describing data integration across services.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and positive evaluation of the work's significance for CPPS design in circular manufacturing. We address each major comment below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract and architecture design] Abstract and architecture description: the central claim that the ontology-grounded knowledge graph functions as the 'factories authoritative write-time state' enabling consistent execution decisions is load-bearing, yet the manuscript provides no mechanisms for concurrency control, conflict resolution under heterogeneous updates, or bounds on reasoning latency; without these, the single-source-of-truth property cannot be assessed for real-time circular manufacturing scenarios where component states are unique and uncertain.

    Authors: We agree that explicit mechanisms for concurrency control, conflict resolution, and reasoning latency bounds are important for validating the single-source-of-truth property in real-time settings. The manuscript presents the knowledge graph in this role at the architectural level to unify heterogeneous and uncertain states as the authoritative backbone, consistent with the derived requirements for handling variability in circular manufacturing. However, detailed implementation mechanisms were not the primary focus. In the revised version we will add a dedicated subsection discussing candidate approaches (e.g., graph versioning, semantic transaction models, and latency bounds derived from standard RDF stores) and explicitly stating the assumptions and limitations for real-time deployment. revision: partial

  2. Referee: [Use cases] Use-case demonstrations: the two implemented use cases show basic integration and reasoning but report no quantitative metrics (e.g., update latencies, reasoning overhead, or consistency error rates) nor comparisons against alternative architectures or baselines, leaving the claim of practical applicability for dynamic plan adaptation unsupported at the level required for the central contribution.

    Authors: The use cases serve as proof-of-concept demonstrations of integration and constraint enforcement rather than as performance benchmarks. Their role is to illustrate how the architecture satisfies the 14 requirements in concrete settings. We acknowledge that the absence of quantitative metrics and baseline comparisons limits the strength of claims about runtime practicality. In revision we will expand the use-case sections to report any measured latencies and overheads from the existing implementations and to clarify the scope of the demonstrations, while noting that systematic benchmarking against alternative architectures lies beyond the design-science focus of the current contribution. revision: partial

Circularity Check

0 steps flagged

No significant circularity; derivation follows independent requirements from external perspectives

full rationale

The paper applies a design science methodology to derive 14 requirements from five complementary external perspectives on circular manufacturing (heterogeneous products, uncertainty, human-machine integration). KAPPS is then constructed to satisfy those requirements, with two use cases providing concrete demonstrations of integration and constraint enforcement, followed by an evaluation that checks alignment with the independently derived requirements. No equations, fitted parameters, or self-citations appear as load-bearing elements; the central claim that the ontology-grounded knowledge graph becomes the authoritative write-time state is presented as an outcome of the design choices rather than a redefinition or tautology. The chain remains self-contained against external benchmarks of manufacturing IT limitations.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities beyond the high-level architecture description are visible.

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Reference graph

Works this paper leans on

63 extracted references · 63 canonical work pages

  1. [1]

    20 (2020).doi: 10.1787/1b38a38f-en

    Organisation for Economic Co-operation and Development (OECD), Improving resource effi- ciency and the circularity of economies for a greener world, OECD Report No. 20 (2020).doi: 10.1787/1b38a38f-en

    work page doi:10.1787/1b38a38f-en 2020

  2. [2]

    United Nations Department of Economic and So- cial Affairs, The Sustainable Development Goals Report 2025, United Nations, 2025.doi:10.183 56/9789211071597

    work page 2025

  3. [3]

    URLhttps://www.ellenmacarthurfounda tion.org/towards-a-circular-economy-b usiness-rationale-for-an-accelerated -transition

    Ellen MacArthur Foundation, Towards the circu- lar economy: Business rationale for an accelerated transition, accessed: 2026-04-30 (2015). URLhttps://www.ellenmacarthurfounda tion.org/towards-a-circular-economy-b usiness-rationale-for-an-accelerated -transition

    work page 2026

  4. [4]

    Potting, M

    J. Potting, M. Hekkert, E. Worrell, A. Hanemaai- jer, Circular economy: Measuring innovation in the product chain, accessed: 2026-04-26 (2017). URLhttps://www.pbl.nl/uploads/defau lt/downloads/pbl-2016-circular-econo my-measuring-innovation-in-product-c hains-2544.pdf

    work page 2026

  5. [5]

    N. Nasr, M. Thurston, Remanufacturing: A key enabler to sustainable product systems, in: Pro- ceedings of the 13th CIRP International Con- ference on Life Cycle Engineering (LCE 2006), 2006, pp. 15–18

    work page 2006

  6. [6]

    Matsumoto, S

    M. Matsumoto, S. Yang, K. Martinsen, Y . Kainuma, Trends and research chal- lenges in remanufacturing, Int J Precis Eng Manuf Green Technol 3 (1) (2016) 129–142. doi:10.1007/s40684-016-0016-4

    work page doi:10.1007/s40684-016-0016-4 2016

  7. [7]

    at - Automa- tisierungstechnik72(9), 774–788 (2024)

    G. Lanza, B. Deml, S. Matthiesen, M. Martin, O. Brützel, R. Hörsting, The vision of the circu- lar factory for the perpetual innovative product, at - Automatisierungstechnik 72 (9) (2024) 774–788. doi:10.1515/auto-2024-0012

    work page doi:10.1515/auto-2024-0012 2024

  8. [10]

    Peffers, T

    K. Peffers, T. Tuunanen, M. A. Rothenberger, S. Chatterjee, A design science research method- ology for information systems research, J Manag Inf Syst 24 (3) (2007) 45–77.doi:10.2753/MI S0742-1222240302

    work page doi:10.2753/mi 2007

  9. [11]

    A. R. Hevner, S. T. March, J. Park, S. Ram, Design science in information systems research, MIS Q. 28 (1) (2004) 75–105

    work page 2004

  10. [12]

    M. P. Groover, Automation, Production Systems, and Computer-Integrated Manufacturing, 5th Edi- tion, Pearson, Hoboken, New Jersey, 2019

    work page 2019

  11. [13]

    Kaiser, D

    J.-P. Kaiser, D. Koch, F. Stamer, J. Peeters, G. Lanza, Semantic 3d product modelling for au- tomated inspection in remanufacturing processes, J Remanuf 16 (1) (2026) 1.doi:10.1007/s132 43-025-00157-8

    work page doi:10.1007/s132 2026

  12. [14]

    Heizmann, J

    M. Heizmann, J. Beyerer, S. Dietrich, L. Hoff- mann, J.-P. Kaiser, G. Lanza, A. Roitberg, R. Stiefelhagen, N. Stricker, H. Wexel, F. Zanger, Managing uncertainty in product and process de- sign for the circular factory, at - Automatisierung- stechnik 72 (9) (2024) 829–843.doi:10.1515/ auto-2024-0009

    work page 2024

  13. [15]

    Hoffmann, M

    L. Hoffmann, M. Heizmann, A. Darijani, J. Bey- erer, Efficient uncertainty propagation in contin- uous bayesian networks using gum and monte carlo methods, in: Proceedings of the 2026 In- ternational Conference on Semantic Computing (ICSC), 2026, pp. 119–123.doi:10.1109/IC SC67292.2026.00023

    work page doi:10.1109/ic 2026

  14. [16]

    Grauberger, M

    P. Grauberger, M. Dörr, G. Lanza, J.-P. Kaiser, A. Albers, T. Düser, L. Tusch, M. Seidler, S. Di- etrich, V . Schulze, S. Matthiesen, Enabling the vi- sion of a perpetual innovative product – predict- ing function fulfillment of new product genera- tions in a circular factory, at - Automatisierung- stechnik 72 (9) (2024) 815–828.doi:10.1515/ auto-2024-0010

    work page 2024

  15. [17]

    Hemmerich, A

    J. Hemmerich, A. Kirn, G. Walter, C. Wittig, P. Grauberger, S. Matthiesen, How to deal with products in circular economy: An approach to model specific design knowledge illustrated at the example of a circular factory for angle grinders, in: Proceedings of the Design Society, V ol. 5, 2025, pp. 1983–1992.doi:10.1017/pds.2025.102 12

    work page doi:10.1017/pds.2025.102 2025

  16. [19]

    Pfrommer, J.-F

    J. Pfrommer, J.-F. Klein, M. Wurster, S. Rapp, P. Grauberger, G. Lanza, A. Albers, S. Matthiesen, J. Beyerer, An ontology for remanufacturing sys- tems, at - Automatisierungstechnik 70 (6) (2022) 534–541.doi:10.1515/auto-2021-0156

    work page doi:10.1515/auto-2021-0156 2022

  17. [20]

    Abadi, Consistency tradeoffs in modern dis- tributed database system design: CAP is only part of the story, Computer 45 (2) (2012) 37–42.doi: 10.1109/MC.2012.33

    D. Abadi, Consistency tradeoffs in modern dis- tributed database system design: CAP is only part of the story, Computer 45 (2) (2012) 37–42.doi: 10.1109/MC.2012.33

    work page doi:10.1109/mc.2012.33 2012

  18. [21]

    Klein, R

    J.-F. Klein, R. Wolf, A. Ernst, Y . Shi, P. Schu- macher, R. Thapa, R. Rayyes, K. Furmans, A knowledge-based intralogistic system for a circu- lar factory, Logist J Proc 21 (2025).doi:10.219 5/lj_proc_klein_202510_01

    work page 2025

  19. [22]

    Koch, J.-P

    D. Koch, J.-P. Kaiser, F. Stamer, R. Stark, G. Lanza, Enhancing visual inspection in remanu- facturing: A reinforcement learning approach with integrated robot simulation, Procedia CIRP 134 (2025) 939–944.doi:10.1016/j.procir.2 025.02.228

    work page doi:10.1016/j.procir.2 2025

  20. [24]

    Dreher, T

    C. Dreher, T. Asfour, Learning symbolic and sub- symbolic temporal task constraints from biman- ual human demonstrations, in: Proceedings of the 2024 IEEE/RSJ International Conference on In- telligent Robots and Systems (IROS), 2024, pp. 5160–5167.doi:10.1109/IROS58592.2024 .10802525. 27

    work page doi:10.1109/iros58592.2024 2024

  21. [25]

    V ogel-Heuser, F

    B. V ogel-Heuser, F. Hartl, M. Wittemer, J. Zhao, A. Mayr, M. Fleischer, T. Prinz, A. Fischer, J. Trauer, P. Schroeder, A.-K. Goldbach, F. Roth- meyer, M. Zimmermann, K.-U. Bletzinger, J. Fot- tner, R. Daub, K. Bengler, A. Borrmann, M. F. Zaeh, K. Wudy, Long living human-machine sys- tems in construction and production enabled by digital twins: exploring ...

    work page doi:10.1515/auto-2023-0227 2024

  22. [26]

    Deutsches Institut für Normung (DIN), DIN SPEC 91345:2016-04, Reference Architecture Model In- dustrie 4.0 (RAMI 4.0), DIN SPEC (2016).doi: 10.31030/2436156

    work page doi:10.31030/2436156 2016

  23. [27]

    Yli-Ojanperä, S

    M. Yli-Ojanperä, S. Sierla, N. Papakonstantinou, V . Vyatkin, Adapting an agile manufacturing con- cept to the reference architecture model Industry 4.0: a survey and case study, J Ind Inf Integr 15 (2019) 147–160.doi:10.1016/j.jii.2018.1 2.002

    work page doi:10.1016/j.jii.2018.1 2019

  24. [28]

    Computer Aided Geometric Design 18(9), 851–863 (2001)

    H. Van Brussel, J. Wyns, P. Valckenaers, L. Bon- gaerts, P. Peeters, Reference architecture for holonic manufacturing systems: PROSA, Comput Ind 37 (3) (1998) 255–274.doi:10.1016/S016 6-3615(98)00102-X

    work page doi:10.1016/s016 1998

  25. [29]

    Najjari, M

    H. Najjari, M. Seitz, E. Trunzer, B. V ogel-Heuser, Cyber-physical production systems for smes: a generic multi-agent-based architecture and case study, in: Proceedings of the 2021 IEEE Inter- national Conference on Industrial Cyber-Physical Systems (ICPS), 2021, pp. 625–630.doi:10.110 9/ICPS49255.2021.9468232

    work page arXiv 2021

  26. [30]

    Macherki, T

    D. Macherki, T. M. L. Diallo, J.-Y . Choley, A. Guizani, M. Barkallah, M. Haddar, QHAR: Q- holonic-based architecture for self-configuration of cyber–physical production systems, Appl Sci 11 (19) (2021) 9013.doi:10.3390/app11199 013

    work page doi:10.3390/app11199 2021

  27. [31]

    Müller, S

    T. Müller, S. Kamm, A. Löcklin, D. White, M. Mellinger, N. Jazdi, M. Weyrich, Architecture and knowledge modelling for self-organized re- configuration management of cyber-physical pro- duction systems, Int J Comput Integr Manuf 36 (12) (2023) 1842–1863.doi:10.1080/09 51192X.2022.2121425

    work page doi:10.1080/09 2023

  28. [32]

    Neubauer, L

    M. Neubauer, L. Steinle, C. Reiff, S. Ajdinovi ´c, L. Klingel, A. Lechler, A. Verl, Architecture for Manufacturing-X: bringing asset administration shell, eclipse dataspace connector and OPC UA together, Manuf Lett 37 (2023) 1–6.doi:10.1 016/j.mfglet.2023.05.002

    work page 2023

  29. [34]

    Zheng, Y

    S. Behrendt, F. Bail, M. Benfer, G. Lanza, aas- middleware: enabling interoperable, real-time in- tegration for smart manufacturing, IFAC-Pap On- line 59 (30) (2025) 671–676.doi:10.1016/j. ifacol.2025.12.315

    work page doi:10.1016/j 2025

  30. [35]

    Rongen, N

    S. Rongen, N. Nikolova, M. Pas, Modelling with AAS and RDF in Industry 4.0, Comput Ind 148 (2023) 103910.doi:10.1016/j.compind.20 23.103910

    work page doi:10.1016/j.compind.20 2023

  31. [36]

    International Electrotechnical Commission, IEC 62264-1:2013 enterprise-control system integra- tion – part 1: Models and terminology, iEC 62264- 1 (2013)

    work page 2013

  32. [37]

    International Electrotechnical Commission (IEC), IEC 61499-1:2012: Function blocks – part 1: ar- chitecture, IEC Standard (2012)

    work page 2012

  33. [38]

    Pérez, E

    F. Pérez, E. Irisarri, D. Orive, M. Marcos, E. Es- tevez, A CPPS architecture approach for Indus- try 4.0, in: Proceedings of the 2015 IEEE 20th Conference on Emerging Technologies and Fac- tory Automation (ETFA), 2015, pp. 1–4.doi: 10.1109/ETFA.2015.7301606

    work page doi:10.1109/etfa.2015.7301606 2015

  34. [39]

    da Rocha, R

    H. da Rocha, R. Abrishambaf, J. Pereira, A. Es- pirito Santo, Integrating the IEEE 1451 and IEC 61499 standards with the industrial internet ref- erence architecture, Sensors 22 (4) (2022) 1495. doi:10.3390/s22041495

    work page doi:10.3390/s22041495 2022

  35. [40]

    Torres, G

    T. Torres, G. Gonçalves, R. Pinto, Literature re- view on cloud-based service-oriented architecture for IEC 61499 distributed control systems, in: Pro- ceedings of the 15th International Conference on Cloud Computing and Services Science, 2025, pp. 174–181.doi:10.5220/0013293400003950

    work page doi:10.5220/0013293400003950 2025

  36. [41]

    Grieves, Digital twin: manufacturing excel- lence through virtual factory replication, White 28 paper, accessed: 2026-04-30 (2014)

    M. Grieves, Digital twin: manufacturing excel- lence through virtual factory replication, White 28 paper, accessed: 2026-04-30 (2014). URLhttps://www.researchgate.net/pub lication/275211047_Digital_Twin_Manufa cturing_Excellence_through_Virtual_Fac tory_Replication

    work page arXiv 2026

  37. [42]

    Kritzinger, M

    W. Kritzinger, M. Karner, G. Traar, J. Henjes, W. Sihn, Digital twin in manufacturing: a cate- gorical literature review and classification, IFAC- Pap Online 51 (11) (2018) 1016–1022.doi: 10.1016/j.ifacol.2018.08.474

    work page doi:10.1016/j.ifacol.2018.08.474 2018

  38. [43]

    B. A. Talkhestani, T. Jung, B. Lindemann, N. Sahlab, N. Jazdi, W. Schloegl, M. Weyrich, An architecture of an intelligent digital twin in a cyber-physical production system, at - Automa- tisierungstechnik 67 (9) (2019) 762–782.doi: 10.1515/auto-2019-0039

    work page doi:10.1515/auto-2019-0039 2019

  39. [44]

    K. T. Park, J. Lee, H.-J. Kim, S. D. Noh, Dig- ital twin-based cyber-physical production system architectural framework for personalized produc- tion, Int J Adv Manuf Technol 106 (5) (2020) 1787–1810.doi:10.1007/s00170-019-046 53-7

    work page doi:10.1007/s00170-019-046 2020

  40. [45]

    L. M. V . da Silva, A. Köcher, M. S. Gill, M. Weiss, A. Fay, Toward a mapping of capability and skill models using asset administration shells and on- tologies, in: Proceedings of the 2023 IEEE 28th International Conference on Emerging Technolo- gies and Factory Automation (ETFA), 2023, pp. 1–4.doi:10.1109/ETFA54631.2023.1027545 9

    work page doi:10.1109/etfa54631.2023.1027545 2023

  41. [46]

    Westermann, M

    T. Westermann, M. S. Gill, A. Fay, Representing timed automata and timing anomalies of cyber- physical production systems in knowledge graphs, in: Proceedings of IECON 2023 – 49th Annual Conference of the IEEE Industrial Electronics So- ciety, 2023, pp. 1–7.doi:10.1109/IECON51785 .2023.10312156

    work page doi:10.1109/iecon51785 2023

  42. [47]

    Müller, N

    T. Müller, N. Sahlab, S. Kamm, C. Köhler, D. Braun, N. Jazdi, M. Weyrich, Context-enriched modeling using knowledge graphs for intelligent digital twins of production systems, in: Proceed- ings of the 2022 IEEE 27th International Confer- ence on Emerging Technologies and Factory Au- tomation (ETFA), 2022, pp. 1–8.doi:10.1109/ ETFA52439.2022.9921615

    work page arXiv 2022

  43. [48]

    G. Wan, X. Dong, Q. Dong, Y . He, P. Zeng, Context-aware scheduling and control architecture for cyber-physical production systems, J Manuf Syst 62 (2022) 550–560.doi:10.1016/j.jm sy.2022.01.008

    work page doi:10.1016/j.jm 2022

  44. [49]

    Novák, S

    P. Novák, S. Biffl, M. Obitko, P. Kadera, Mit- igating undesired conditions in flexible produc- tion with product–process–resource asset knowl- edge graphs, in: Proceedings of the Joint Proceed- ings of Industry, Doctoral Consortium, Posters and Demos of the 24th International Semantic Web Conference (ISWC-C 2025), V ol. 4085 of CEUR Workshop Proceedings,...

    work page 2025

  45. [50]

    Järvenpää, N

    E. Järvenpää, N. Siltala, O. Hylli, H. Nylund, M. Lanz, Semantic rules for capability matchmak- ing in the context of manufacturing system design and reconfiguration, Int J Comput Integr Manuf 36 (1) (2023) 128–154.doi:10.1080/095119 2X.2022.2081361

    work page doi:10.1080/095119 2023

  46. [51]

    Hofmann, S

    C. Hofmann, S. Staab, M. Selzer, G. Neumann, K. Furmans, M. Heizmann, J. Beyerer, G. Lanza, J. Pfrommer, T. Düser, J.-F. Klein, The role of an ontology-based knowledge backbone in a cir- cular factory, at - Automatisierungstechnik 72 (9) (2024) 875–883.doi:10.1515/auto-2024-0 006

    work page doi:10.1515/auto-2024-0 2024

  47. [52]

    Bail, J.-F

    F. Bail, J.-F. Klein, R. B. Thapa, Core ontology for the circular factory, Ontology, version 1.0 (2026). URLhttps://w3id.org/circularfactory /Core

    work page 2026

  48. [53]

    Darijani, J

    A. Darijani, J. Beyerer, Z. S. H. Nasrollah, L. Hoffmann, M. Heizmann, Comprehensive de- scription of uncertainty in measurement for repre- sentation and propagation with scalable precision, arXiv preprint (2026).doi:10.48550/arXiv.2 603.20365

    work page doi:10.48550/arxiv.2 2026

  49. [54]

    Brandt, L

    N. Brandt, L. Griem, C. Herrmann, E. Schoof, G. Tosato, Y . Zhao, P. Zschumme, M. Selzer, Kadi4mat: A research data infrastructure for ma- terials science, Data Sci J 20 (2021) 8.doi: 10.5334/dsj-2021-008

    work page doi:10.5334/dsj-2021-008 2021

  50. [55]

    Knublauch, D

    H. Knublauch, D. Kontokostas, Shapes constraint language (SHACL), W3C Recommendation, ac- cessed: 2026-04-30 (2017). URLhttps://www.w3.org/TR/shacl/

    work page 2026

  51. [56]

    T. Lebo, S. Sahoo, D. McGuinness, K. Belhaj- jame, J. Cheney, D. Corsar, D. Garijo, S. Soiland- Reyes, S. Zednik, J. Zhao, PROV-O: the PROV on- tology, W3C Recommendation, accessed: 2026- 29 04-30 (2013). URLhttps://www.w3.org/TR/prov-o/

    work page 2026

  52. [57]

    Behrendt, F

    S. Behrendt, F. Stamer, M. C. May, G. Lanza, To- wards a service-oriented architecture for produc- tion planning and control: a comprehensive re- view and novel approach, in: Proceedings of the 5th Conference on Production Systems and Logis- tics (CPSL-2), 2023, pp. 255–267.doi:10.154 88/15271

    work page 2023

  53. [58]

    Köcher, A

    A. Köcher, A. Belyaev, J. Hermann, J. Bock, K. Meixner, M. V olkmann, M. Winter, P. Zim- mermann, S. Grimm, C. Diedrich, A reference model for common understanding of capabilities and skills in manufacturing, at - Automatisierung- stechnik 71 (2) (2023) 94–104.doi:10.1515/au to-2022-0117

    work page doi:10.1515/au 2023

  54. [59]

    Wright, S

    J. Wright, S. Rodríguez Méndez, A. Haller, K. Taylor, P. G. Omran, on2ts: Typescript gener- ation from OWL ontologies and SHACL, in: Pro- ceedings of the 19th International Semantic Web Conference (ISWC) Demos and Industry Tracks, V ol. 2721 of CEUR Workshop Proceedings, 2020, pp. 359–363

    work page 2020

  55. [60]

    W. Xiao, T. Qiu, J. Guo, G. Zhao, Metafactory: a cloud-based framework to configure and gen- erate dynamic data structures from the STEP-NC knowledge graph, J Manuf Syst 80 (2025) 89–107. doi:10.1016/j.jmsy.2025.02.012

    work page doi:10.1016/j.jmsy.2025.02.012 2025

  56. [61]

    J.-B. Lamy, Owlready: ontology-oriented pro- gramming in python with automatic classification and high-level constructs for biomedical ontolo- gies, Artif Intell Med 80 (2017) 11–28.doi: 10.1016/j.artmed.2017.07.002

    work page doi:10.1016/j.artmed.2017.07.002 2017

  57. [62]

    Vlajic, A

    K. Vlajic, A. Martin, M. Fischer, S. E. Schwarz, T. Düser, A. Albers, Development and evaluation of an ontology for model-based cross-generational modeling of products and production systems in circular economy, in: Proceedings of the 2025 IEEE International Symposium on Systems Engi- neering (ISSE), 2025, pp. 1–7.doi:10.1109/IS SE65546.2025.11370110

    work page doi:10.1109/is 2025

  58. [63]

    Vlajic, Perpetual product ontology for the cir- cular factory, Ontology (2026)

    K. Vlajic, Perpetual product ontology for the cir- cular factory, Ontology (2026). URLhttps://w3id.org/circularfactory /PerpetualProduct

    work page 2026

  59. [64]

    S. H. Mayer, Development of a completely de- centralized control system for modular continu- ous conveyors, Ph.D. thesis, Karlsruhe Institute of Technology (KIT), formerly Universität Karlsruhe (TH) (2009).doi:10.5445/KSP/1000011463

    work page doi:10.5445/ksp/1000011463 2009

  60. [65]

    Wagner, Validating system behavior in OPC UA using semantic web technologies, IFAC-Pap Online 59 (25) (2025) 155–160.doi:10.1016/ j.ifacol.2025.11.941

    M. Wagner, Validating system behavior in OPC UA using semantic web technologies, IFAC-Pap Online 59 (25) (2025) 155–160.doi:10.1016/ j.ifacol.2025.11.941

    work page 2025

  61. [66]

    David, E

    J. David, E. Coatanéa, A. Lobov, Deploying OWL ontologies for semantic mediation of mixed- reality interactions for human–robot collaborative assembly, J Manuf Syst 70 (2023) 359–381.doi: 10.1016/j.jmsy.2023.07.013

    work page doi:10.1016/j.jmsy.2023.07.013 2023

  62. [67]

    Moreno, T

    T. Moreno, T. Sobral, A. Almeida, A. L. Soares, A. Azevedo, Semantic asset administration shell towards a cognitive digital twin, in: Flexible Au- tomation and Intelligent Manufacturing: Estab- lishing Bridges for More Sustainable Manufactur- ing Systems, 2024, pp. 679–686.doi:10.1007/ 978-3-031-38165-2_79

    work page 2024

  63. [68]

    Elfaham, U

    H. Elfaham, U. Epple, Meta models for intralogis- tics: base for topology planning and graph genera- tion of product flow, at - Automatisierungstechnik 68 (3) (2020) 208–221.doi:10.1515/auto-2 019-0083. 30

    work page doi:10.1515/auto-2 2020