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Information fusion and systematic logic library-generation methods for self-configuration of autonomous digital twin

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Abstract

Self-configuration is the preparation required to facilitate smart-manufacturing (SM) with the inputs derived without user intervention for engineering applications. Thus, it is vital for achieving the highest maturity level of SM technologies. In context, digital twin (DT) is an advanced virtual factory with simulation as its core technical functionality. However, the requirement of several inputs limits the implementation of DT on a physical asset without user intervention. Moreover, surpassing this limitation requires extraction methods for deriving the necessary inputs for DT application. Therefore, this study proposes information fusion and systematic logic library (SLL)-generation methods to facilitate the self-configuration of an autonomous DT. The information fusion aggregates and extracts the information elements required for DT application from heterogeneous information sources. In addition, the SLL generation method created the SLL required for reflecting the functional units of agents within the physical asset. Both methods were proposed from available SM standards such as ISA-95, automation markup language, and OPC unified architecture. Furthermore, an autonomous DT-supporting framework was designed by analyzing the relationship between asset description and SM standards, which facilitated the artificial intelligence-based extraction of the asset description object and SLL. Additionally, the core functional engines within this framework were designed using machine learning and process-mining techniques. Consequently, the proposed methods reduced the input pre-processing time required for constructing and synchronizing an autonomous DT to aid the application of autonomous DT on the physical asset without user intervention.

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Abbreviations

A-IoT:

Autonomous internet of things

AAS:

Asset administration shell

AG:

Aging

AI:

Artificial intelligence

AML:

Automation markup language

ANN:

Artificial neural network

BOM:

Bill of materials

CDL:

Configuration data library

CMSD:

Core manufacturing simulation data

CPS:

Cyber physical system

CSPI:

Commercial off-the-shelf simulation package interoperability

DB:

Database

DDL:

Data description language

DT:

Digital twin

ERP:

Enterprise resource planning

LIB:

Lithium-ion battery

M2M:

Machine-to-machine

MES:

Manufacturing execution system

MHC:

Material handling conveyor

MHE:

Material handling equipment

MHR:

Material handling robot

MHV:

Material handling vehicle

ML:

Machine learning

NESIS:

Neutral simulation schema

OPC UA:

OPC unified architecture

RAMI:

Reference architectural model industries

SL:

Supervised learning

SM:

Smart manufacturing

SMOTE:

Synthetic minority oversampling technique

SOA:

Service-oriented architecture

SOAP:

Simple object access protocol

VREDI:

Virtual representation for digital twin application

WCF:

Windows communication foundation

WIP:

Work in process

XML:

Extensible markup language

i :

Index of products

j :

Index of machines

k :

Index of material handling equipment (MHE)

l :

Index of event logs and process discovery result

m :

Index of process plans

n :

Index of list of process operation

L :

Event log information in trace information repository

epoch:

Selected epoch for policy neural networks

π(a|s)k , n :

Policy neural network for policy of MHE k after process operation \(n\)

A L p :

List of activities in event log Lp

\({A^S_{L^P}}\) :

Start activity in event log Lp

\({A^e_{L^P}}\) :

End activity in event log Lp

\({AC}_{{L}_{k,l}^{d}}\) :

Activity-action list with result index \(l\) in process-discovery result \({L}^{d}\) for MHE \(k\)

a :

Possible action

L p :

Pre-processed event log information derived from log \(L\)

L d :

Process discovery result derived from log \({L}^{p}\)

LO k :

Systematic logic library generated for MHE \(k\)

P i :

Process plan information designed for product \(i\)

SEk :

List of MHE \(k\) -extracted source elements

\(ST_{{L_{k,l}^{d} }}\) :

Activity-state list with result index \(l\) of process discovery result Ld for MHE k

s :

List of states in policy network

TEk , n :

List of extracted target elements of MHE \(k\) after process operation n

L p :

List of direct successions containing directly followed activities in event log Lp

References

  • Adolphs, P., Auer, S., Bedenbender, H., Billmann, M., Hankel, M., Heidel, R., et al. (2016). Structure of the administration shell continuation of the development of the reference model for the industry 4.0 component. Brochure. ZVEI and VDI Status Report. https://www.academia.edu/35536663/Structure_of_the_Administration_Shell_Continuation_of_the_Development_of_the_Reference_Model_for_the_Industrie_4.0_Component.pdf. Retrieved May 28, 2020.

  • Alam, K. M., & El Saddik, A. (2017). C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access, 5, 2050–2062.

    Article  Google Scholar 

  • Barschdorff, D., & Monostori, L. (1991). Neural networks—Their applications and perspectives in intelligent machining. Computers in Industry, 17(2–3), 101–119.

    Article  Google Scholar 

  • Bedenbender, H., Billmann, M., Epple, U., Hadlich, T., Hankel, M., Heidel, R., et al. (2017). Examples of the asset administration shell for Industry 4.0 components–Basic part. ZVEI white paper. https://www.zvei.org/fileadmin/user_upload/Presse_und_Medien/Publikationen/2017/April/Asset_Administration_Shell/ZVEI_WP_Verwaltungschale_Englisch_Download_03.04.17.pdf. Accessed May 28, 2020

  • Bloomfield, R., Mazhari, E., Hawkins, J., & Son, Y. J. (2012). Interoperability of manufacturing applications using the Core Manufacturing Simulation Data (CMSD) standard information model. Computers and Industrial Engineering, 62(4), 1065–1079.

    Article  Google Scholar 

  • Cai, Y., Starly, B., Cohen, P., & Lee, Y. S. (2017). Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing. Procedia Manufacturing, 10, 1031–1042.

    Article  Google Scholar 

  • Cheng, Y., Zhang, Y., Ji, P., Xu, W., Zhou, Z., & Tao, F. (2018). Cyber-physical integration for moving digital factories forward towards smart manufacturing: A survey. International Journal of Advanced Manufacturing Technology, 97(1–4), 1209–1221.

    Article  Google Scholar 

  • Ding, K., Chan, F. T. S., Zhang, X., Zhou, G., & Zhang, F. (2019). Defining a digital twin-based cyber-physical production system for autonomous manufacturing in smart shop floors. International Journal of Production Research, 57(20), 6315–6334.

    Article  Google Scholar 

  • Dorst, W., ed. (2015). Umsetzungsstrategie Industry 4.0: Ergebnisbericht der Plattform Industry 4.0. Bitkom Research GmbH.

  • Gabor, T., Belzner, L., Kiermeier, M., Beck, M. T., & Neitz, A. (2016). A simulation-based architecture for smart cyber-physical systems. In 2016 IEEE International Conference on Autonomic Computing (ICAC) (pp. 374–379).

  • Gökalp, E., Şener, U., & Eren, P. E. (2017). Development of an assessment model for industry 4.0: Industry 4.0-MM. In. Communications in Computer and Information Science International Conference on Software Process Improvement and Capability Determination (pp. 128–142).

  • Grieves, M. (2014). Digital twin: Manufacturing excellence through virtual factory replication. White paper (pp. 1–7).

  • Hankel, M., & Rexroth, B. (2015). The reference architectural model industry 4.0 (rami 4.0). ZVEI.

  • Huang, S. H., & Hong-Chao Zhang, H.-C. (1994). Artificial neural networks in manufacturing: Concepts, applications, and perspectives. IEEE Transactions on Components, Packaging, and Manufacturing Technology: Part A, 17(2), 212–228.

    Article  Google Scholar 

  • IEC. (2003). IEC 62264-1: Enterprise-control system integration—Part 1: Models and terminology.

  • IEC. (2014). IEC 62714-1: Engineering data exchange format for use in industrial automation systems engineering–Automation markup language—Part 1: Architecture and general requirements.

  • IEC. (2015). IEC 62714-2: Engineering data exchange format for use in industrial automation systems engineering—Automation markup language—Part 2: Role class libraries.

  • IEC. (2016). IEC 62264-3: Enterprise-control system integration—Part 3: Activity models of manufacturing operations management.

  • Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846.

    Article  Google Scholar 

  • Jeon, B., Yoon, J. S., Um, J., & Suh, S. H. (2020). The architecture development of Industry 4.0 compliant smart machine tool system (SMTS). Journal of Intelligent Manufacturing, 31(8), 1837–1859. https://doi.org/10.1007/s10845-020-01539-4

    Article  Google Scholar 

  • Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRy 4.0: Securing the future of German manufacturing industry; final report of the Industry 4.0 Working Group. Forschungsunion.

  • Kim, D.-Y., Park, J.-W., Baek, S., Park, K.-B., Kim, H.-R., Park, J.-I., et al. (2020). A modular factory testbed for the rapid reconfiguration of manufacturing systems. Journal of Intelligent Manufacturing, 31(3), 661–680.

    Article  Google Scholar 

  • Kohonen, T. (1988). An introduction to neural computing. Neural Networks, 1(1), 3–16.

    Article  Google Scholar 

  • Lee, J. Y., Kang, H. S., Kim, G. Y., & Noh, S. D. (2012). Concurrent material flow analysis by P3R-driven modeling and simulation in PLM. Computers in Industry, 63(5), 513–527.

    Article  Google Scholar 

  • Lee, J. Y., Kang, H. S., Noh, S. D., Woo, J. H., & Lee, P. (2011). NESIS: A neutral schema for a web-based simulation model exchange service across heterogeneous simulation software. International Journal of Computer Integrated Manufacturing, 24(10), 948–969.

    Article  Google Scholar 

  • Liu, Z., Meyendorf, N., & Mrad, N. (2018). The role of data fusion in predictive maintenance using digital twin. In AIP Conference Proceedings (Vol 1, 020023). AIP Publishing LLC, 1949.

  • Malik, O., Ramchurn, S. D., Fuentes, C., Fischer, J., Crabtree, A., Nowacka, D., et al. (2018). Poster. Everyday interaction with autonomous internet of things. In International joint conference on artificial intelligence. Stockholm, Sweden.

  • MESA. (2010). (IEC 62264-2 Mod) Enterprise-control system integration—Part 2: Object model attributes. ISA 95. American National Standard ANSI, 00, 02

  • Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49, 194–214.

    Article  Google Scholar 

  • OPC foundation, Part 1: OPC UA Specification: Part 1—Overview and Concepts.

  • OPC foundation, Part 5: OPC UA Specification: Part 5—Information Model.

  • Park, K. T., Lee, D., & Noh, S. D. (2020a). Operation procedures of a work-center-level digital twin for sustainable and smart manufacturing. International Journal of Precision Engineering and Manufacturing-Green Technology, 7(3), 791–814.

    Article  Google Scholar 

  • Park, K. T., Lee, J., Kim, H. J., & Noh, S. D. (2020b). Digital twin-based cyber physical production system architectural framework for personalized production. The International Journal of Advanced Manufacturing Technology, 106(5–6), 1787–1810.

    Article  Google Scholar 

  • Park, K. T., Nam, Y. W., Lee, H. S., Im, S. J., Noh, S. D., Son, J. Y., & Kim, H. (2019). Design and implementation of a digital twin application for a connected micro smart factory. International Journal of Computer Integrated Manufacturing, 32(6), 596–614.

    Article  Google Scholar 

  • Park, K. T., Son, Y. H., & Noh, S. D. (2020c). The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1788738

    Article  Google Scholar 

  • Park, K. T., Yang, J., & Noh, S. D. (2021). VREDI: Virtual representation for a digital twin application in a work-center-level asset administration shell. Journal of Intelligent Manufacturing, 32, 501–544. https://doi.org/10.1007/s10845-020-01586-x

    Article  Google Scholar 

  • Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, 3585–3593.

    Article  Google Scholar 

  • Qin, J., Liu, Y., & Grosvenor, R. (2016). A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP, 52, 173–178.

    Article  Google Scholar 

  • Riddick, F. H., & Lee, Y. T. (2010). Core manufacturing simulation data (CMSD): A standard representation for manufacturing simulation-related information. In Fall simulation interoperability workshop SISO (pp. 20–24).

  • Rosen, R., Von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine, 48(3), 567–572.

    Article  Google Scholar 

  • SISO (2010). SISO-STD-006-2010: Standard for commercial off-the-shelf (COTS) simulation package interoperability (CSPI) reference models.

  • Siso, S. T. D. (2012). 008-01-2012: Standard for core manufacturing simulation data –XML representation. Simulation Interoperability Standards Organization.

  • Suri, K., Cadavid, J., Alferez, M., Dhouib, S., & Tucci-Piergiovanni, S. (2017). Modeling business motivation and underlying processes for RAMI 4.0-aligned cyber-physical production systems. In 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1–6).

  • Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 94(9–12), 3563–3576.

    Article  Google Scholar 

  • Tao, F., & Zhang, M. (2017). Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing. IEEE Access, 5, 20418–20427.

    Article  Google Scholar 

  • Tao, F., Zhang, M., & Nee, A. Y. C. (2019). Digital twin driven smart manufacturing. Academic Press.

    Google Scholar 

  • Timo, D.-I.F., Mónica, R., Christian, B., Friedrich, M., Bernd, K., Urlich, D. B., & Waldemar, S. (2016). Agent-based communication to map and exchange shop floor data between MES and material flow simulation based on the open standard CMSD. IFAC-PapersOnLine, 49(12), 1526–1531.

    Article  Google Scholar 

  • Uhlemann, T.H.-J., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017). The digital twin: Demonstrating the potential of real time data acquisition in production systems. Procedia Manufacturing, 9, 113–120.

    Article  Google Scholar 

  • van der Aalst, W. (2016). Data science in action. In Process mining (pp. 3–23). Springer.

  • Wang, T., Qiao, M., Zhang, M., Yang, Y., & Snoussi, H. (2018). Data-driven prognostic method based on self-supervised learning approaches for fault detection. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1431-x

    Article  Google Scholar 

  • Weber, C., Königsberger, J., Kassner, L., & Mitschang, B. (2017). M2DDM–a maturity model for data-driven manufacturing. Procedia CIRP, 63, 173–178.

    Article  Google Scholar 

  • Wiktorsson, M., Noh, S. D., Bellgran, M., & Hanson, L. (2018). Smart factories: South Korean and Swedish examples on manufacturing settings. Procedia Manufacturing, 25, 471–478.

    Article  Google Scholar 

  • Yoon, S., Um, J., Suh, S. H., Stroud, I., & Yoon, J. S. (2019). Smart factory information service bus (SIBUS) for manufacturing application: Requirement, architecture and implementation. Journal of Intelligent Manufacturing, 30(1), 363–382.

    Article  Google Scholar 

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Funding

This work was partly supported by the Technology Innovation Program (20003957, Simulation and Optimization of Logistics Operation of Big Data Based Manufacturing Line) funded by the Ministry of Trade, Industry & Energy (MOTIE). Moreover, this study was supported by the Smart Factory Collaboration Package Technology Development Program (20004170, Development of Optimal Productivity Prediction Technology Based on Collaboration of Human and Machine) funded by the MOTIE and Korea Institute for Advancement of Technology (KIAT).

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Correspondence to Sang Do Noh.

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Appendix

Appendix

See Tables 6

Table 6 Manifest of Product class for work-center-level DT.

, 7

Table 7 Manifest of Resource class for work-center-level DT (Park et al., 2020a, 2020b, 2020c)

, 8

Table 8 Manifest of Process class for work-center-level DT.

, 9

Table 9 Manifest of Plan class for work-center-level DT.

, 10

Table 10 Manifest of Plant class for work-center-level DT (Park et al., 2020a, 2020b, 2020c)

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Park, K.T., Lee, S.H. & Noh, S.D. Information fusion and systematic logic library-generation methods for self-configuration of autonomous digital twin. J Intell Manuf 33, 2409–2439 (2022). https://doi.org/10.1007/s10845-021-01795-y

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