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
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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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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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DOI: https://doi.org/10.1007/s10845-021-01795-y