Digital Twins Supporting Efficient Digital Industrial Transformation
Abstract
:1. Introduction
- The CTs, and in particular, the ability of each CT to digitally represent a specific complex industrial machine via a semantic description of the machine, to communicate with the corresponding machines to obtain its data and send machine settings and other actuation to the machine, and to simulate the machine operation to facilitate Industry 4.0 application testing;
- CT-based Industry 4.0 application development that utilizes CTs instead of individual sensors and actuators to make Industry 4.0 application development more cost-efficient. Unlike IoT platform-based Industry 4.0 application development that can integrate the individual sensors and actuators of the machine, CT-based application development can integrate entire-complex machines reducing the cost of application development, especially when CTs are used by multiple Industry 4.0 applications;
- A novel cost model for estimating the cost of developing an Industry 4.0 application. To the best of our knowledge, no other model currently exists for that.
- An evaluation of CT-based applications using a sample application from the dairy industry that shows the benefits of CT-based Industry 4.0 application development.
2. Related Work
2.1. DTs and DT-Based Industry 4.0 Application Development
2.2. Traditional Industry 4.0 Application Development
2.3. Industry 4.0 Application Development Cost Modelling
2.4. Summary
3. Cyber Twins (CTs)
- Query the semantic description of the machine that is represented by the CT;
- Communicate with the machine including obtaining the data produced by the machine, applying the machine settings, and sending other inputs to the machine;
- Interact with the machine emulator or simulator that is incorporated in the CT to support application testing.
3.1. CT Ontology for Describing Complex Industrial Machines
3.2. CT Management Framework
3.3. Simulators in CTs and Their Support for Testing
4. CT-Based Industry 4.0 Application Development and Costing
4.1. CT-Based Industry 4.0 Application Development: Activities and Roles
4.1.1. Developing a New CT-Based Industry 4.0 Application
4.1.2. Updating an Existing CT-Based Industry 4.0 Application
4.1.3. Porting an Existing CT-Based Industry 4.0 Application
4.2. A Cost Model for Industry 4.0 Application Development
4.2.1. Cost of Identifying the Machine Data Required by the Application
4.2.2. Cost of Integrating Machines and Machine Data with the Application
4.2.3. Cost of Developing the Application Functionality and Testing the Application
4.2.4. Degree of Adaptability of an Industry 4.0 Application
4.2.5. Degree of Portability of an Industry 4.0 Application
5. Experimental Evaluation of a CT-Based Industry 4.0 Application
5.1. CT Ontology-Based Models of the Pickup Truck and the Milk Tank Machines
5.2. CTs of the Pickup Truck and the Milk Tank
5.2.1. CT Descriptions of the Milk Tank and Pickup Truck Machines
5.2.2. Machine Simulators of the Milk Tank and Pickup Truck
5.3. CT-Based Milk Pickup Monitoring Application Development
5.3.1. Developing a New CT-Based Milk Pickup Monitoring Application
5.3.2. Updating an Existing CT-Based Milk Pickup Monitoring Application
5.3.3. Porting the CT-Based Milk Pickup Monitoring Application to a Different Milk Farm
5.4. Experimentally Evaluating CT-Based Industry 4.0 Application Development
5.4.1. Experimental Setup and Methodology for Experiments
5.4.2. Estimating the Costs of New Industry 4.0 Application Development
- When developing the CT-based application we assumed that and were already integrated with their CTs. Therefore, the CT generation cost was not considered in the calculation of application development cost;
- In Azure IoT we assumed and were already integrated with the Azure IoT platform via the Azure IoT Hub [60]. Therefore, the cost of integration with the IoT platform was not considered in the calculation of the application development cost. Moreover, we assumed that and had sufficient and accurate machine descriptions associated with them that could be directly used by the application developer.
5.4.3. Estimating the Costs of Industry 4.0 Application Update
- When updating to use , we assumed that the CT of was updated to connect with without generating a new CT for ;
- When updating the application using Azure, we assumed that was already integrated with the same IoT Hub instance as and had a different device id.
5.4.4. Estimating the Costs of Industry 4.0 Application Porting
- When porting that uses CTs to we assumed that CTs were available for and ;
- When porting using Azure IoT, we assumed and connected to a different IoT Hub and had different device ids, and they were already connected with the IoT Hub.
5.5. Discussion
6. Conclusions and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Notation | Description |
---|---|
Set of applications () | |
Set of machines in a plant () | |
Set of inputs | |
Set of outputs | |
Set of machines that are utilized by () | |
Set of inputs provided by , to | |
Set of outputs retrieved by , from | |
Set of sensors in | |
Set of actuators in | |
Set of machine automation/machine controls in | |
Set of machine inputs consumed by () | |
Set of machine outputs produced by () | |
Set of machine settings consumed by () | |
Set of data translators | |
Set of data translators used by for translating machine outputs produced by | |
The total cost of developing by using | |
Cost of identifying the data needed by from | |
Cost of integrating and their data with | |
Cost of developing and testing using |
Notation | Description |
---|---|
Environment being monitored | |
Pickup monitoring application | |
Pickup truck | |
Machine outputs of | |
Milk tank | |
Machine outputs of | |
Machine input of | |
Pickup event generator machine automation of |
Industry 4.0 Application | Identify (NoQ) | Identify (NoC) | Integrate (NoC) | Integrate (SloC) | Dev and Test (NoC) | Dev and Test (SloC) | Total Cost ($) |
---|---|---|---|---|---|---|---|
IoT platform | 0 | 2 | 4 | 42 | 2 | 159 | 3762.00 |
CTs | 0 | 2 | 3 | 32 | 0 | 112 | 2682.00 |
Number of Milk Tanks | Number of Milk Trucks | Cost of Identifying | Cost of Integration | Cost of Dev and Test | Total Cost ($) |
---|---|---|---|---|---|
20 | 2 | 396.00 | 2286.00 | 4050.00 | 6732.00 |
40 | 4 | 792.00 | 3402.00 | 5202.00 | 9396.00 |
60 | 6 | 1188.00 | 4518.00 | 6354.00 | 12,060.00 |
80 | 8 | 1584.00 | 5634.00 | 7506.00 | 14,724.00 |
100 | 10 | 1980.00 | 6750.00 | 8658.00 | 17,388.00 |
Number of Milk Tanks | Number of Milk Trucks | Cost of Identifying | Cost of Integration | Cost of Dev and Test | Total Cost ($) |
---|---|---|---|---|---|
20 | 2 | 396.00 | 1332.00 | 2016.00 | 3744.00 |
40 | 4 | 792.00 | 2088.00 | 2016.00 | 4896.00 |
60 | 6 | 1188.00 | 2844.00 | 2016.00 | 6048.00 |
80 | 8 | 1584.00 | 3600.00 | 2016.00 | 7206.00 |
100 | 10 | 1980.00 | 4356.00 | 2016.00 | 8352.00 |
Industry 4.0 Application | Identify (NoQ) | Identify (NoC) | Integration (NoC) | Integration (SloC) | Dev and Test (NoC) | Dev and Test (SloC) | Total Cost ($) |
---|---|---|---|---|---|---|---|
IoT platform | 0 | 1 | 0 | 2 | 3 | 0 | 90.00 |
CTs | 0 | 1 | 1 | 0 | 0 | 0 | 36.00 |
Changed Machines/Total No. of Machines | Cost of Updating | Cost of Redev. | Degree of Adaptability |
---|---|---|---|
22/110 | 2736.00 | 17,388.00 | 0.8426 |
44/110 | 5472.00 | 17,388.00 | 0.6853 |
66/110 | 8208.00 | 17,388.00 | 0.5279 |
88/110 | 10,944.00 | 17,388.00 | 0.3706 |
110/110 | 13,680.00 | 17,388.00 | 0.2133 |
Changed Machines/Total No. of Machines | Cost of Updating | Cost of Redev. | Degree of Adaptability |
---|---|---|---|
22/110 | 792.00 | 8352.00 | 0.9052 |
44/110 | 1584.00 | 8352.00 | 0.8103 |
66/110 | 2376.00 | 8352.00 | 0.7156 |
88/110 | 3168.00 | 8352.00 | 0.6207 |
110/110 | 3960.00 | 8352.00 | 0.5259 |
Industry 4.0 Application | Identify (NoQ) | Identify (NoC) | Integration (NoC) | Integration (SLoC) | Dev and Test (NoC) | Dev and Test (SLoC) | Total Cost ($) |
---|---|---|---|---|---|---|---|
Azure IoT | 0 | 2 | 4 | 3 | 2 | 4 | 270.00 |
CTs | 0 | 2 | 3 | 0 | 0 | 0 | 90.00 |
No. of Machines | Cost of Porting | Cost of Redev. | Degree of Portability |
---|---|---|---|
22 | 2358.00 | 6732.00 | 0.6497 |
44 | 4662.00 | 9396.00 | 0.5039 |
66 | 6966.00 | 12,060.00 | 0.4224 |
88 | 9270.00 | 14,724.00 | 0.3704 |
110 | 11,574.00 | 17,388.00 | 0.3344 |
No. of Machines | Cost of Porting | Cost of Redev. | Degree of Portability |
---|---|---|---|
22 | 1152.00 | 3744.00 | 0.6923 |
44 | 2304.00 | 4896.00 | 0.5294 |
66 | 3456.00 | 6048.00 | 0.4256 |
88 | 4608.00 | 7200.00 | 0.3600 |
110 | 5760.00 | 8352.00 | 0.3103 |
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Bamunuarachchi, D.; Georgakopoulos, D.; Banerjee, A.; Jayaraman, P.P. Digital Twins Supporting Efficient Digital Industrial Transformation. Sensors 2021, 21, 6829. https://doi.org/10.3390/s21206829
Bamunuarachchi D, Georgakopoulos D, Banerjee A, Jayaraman PP. Digital Twins Supporting Efficient Digital Industrial Transformation. Sensors. 2021; 21(20):6829. https://doi.org/10.3390/s21206829
Chicago/Turabian StyleBamunuarachchi, Dinithi, Dimitrios Georgakopoulos, Abhik Banerjee, and Prem Prakash Jayaraman. 2021. "Digital Twins Supporting Efficient Digital Industrial Transformation" Sensors 21, no. 20: 6829. https://doi.org/10.3390/s21206829