Digital Twins: Enabling Interoperability in Smart Manufacturing Networks
2. Interoperability in Manufacturing
- Technical Complexity: Manufacturing systems often consist of a variety of different equipment, machines, and software applications from different vendors, each with its own data format and communication protocols. Achieving interoperability between these systems can be technically complex and challenging.
- Data Incompatibility: Incompatible data formats and standards can make it difficult to integrate different systems and share data between them. Data formats, protocols, and standards can vary widely across different manufacturing systems, leading to incompatibility issues.
- Security Risks: Interoperability between different systems and devices can increase the risk of security breaches, as it creates more opportunities for hackers to exploit vulnerabilities.
- Lack of Standardisation: There is currently no single standard for achieving interoperability in manufacturing, which can lead to confusion and incompatibility issues between different systems and devices.
- Cost: Achieving interoperability can be expensive, as it often requires significant investment in hardware, software, and personnel resources.
- Legacy Systems: Many manufacturing systems and equipment are older and may not have been designed with interoperability in mind. Retrofitting these systems to achieve interoperability can be costly and time-consuming.
- Organizational Resistance: Achieving interoperability often requires changes in business processes and workflows, which can be met with resistance from employees and management.
2.1. Backwards and Forward Compatibility
2.2. Semantic Interoperability
2.3. Syntactic Interoperability
2.4. Homogenising Data for Interoperability
3. Digital Twins
“Only when we get it to where it performs to our requirements do we physically manufacture it? We then want that physical build to tie back to its digital twin through sensors so that the digital twin contains all the information that we could have by inspecting the physical build.”Dr Michael Grieves, 2002.
- Digital Model: A manual data exchange between a physical object and a digital object is required, and therefore changes in the physical object are not reflected in real-time.
- Digital Shadow: Data from the physical object is automatically transferred to the digital counterpart, but not the other way around. Therefore, changes in the physical object can be viewed digitally but not vice-versa.
- Digital Twin: A two-way data exchange between physical and digital objects is involved. Therefore, the changes in the physical/digital objects affect each other.
- Part/Component Twin: the smallest unit on the industry floor and is based on a geometric, functional, and operational model of the unit-level physical copy.
- System level: DT is the composition of many unit-level DTs in a production floor, interconnected for wider information flow and efficient resource allocation.
- Digital twin prototype (DTP) is the collection of information needed to generate a physical model from the virtual version. This consists of design documents, CAD files etc. The product cycle starts from the creation of the DTP, tested rigorously, before creating its physical twin. The DTP helps identify unwanted outcomes which are impossible to identify with traditional prototyping.
- Digital twin instance is linked to its physical replica throughout the duration of its life. To identify and predict the performance of a physical system after it has been constructed, data collected at the physical layer is communicated to the virtual space and vice versa. With the available data, it can be investigated if the prediction model is as expected or not.
3.1. Applications of Digital Twins
- Product DT analyses the product in different conditions and ensures that the physical product is acting as expected. This virtual validation of the product leads to rapid prototyping and reduced development time.
- Production DT is used to validate the processes through simulation and analysis, before beginning actual production, which paves the way for the creation of a flexible production approach. The product and production DT data can be utilised to track and maintain the equipment.
- Performance DT is used for decision-making through data collection and analysis. Performance DT incorporates product and production performances and therefore it optimises the functioning of the industry floor according to the obtainability of the industry resources. This creates an option to boost the performance of both production and product DT using a feedback loop.
- Partial Digital Twins consist of only a small amount of data from their physical counterpart.
- Clone Digital Twins consist of a significant amount of data from the physical system useful for making prototypes.
- Augmented Digital Twins use the collected data from the asset along with the historical data and derive useful information using data analysis.
- Pre-digital Twin is the first level where the DT is created before the physical system to analyse prototype designs and rule out any technical risks by virtual commissioning.
- Digital Twin is the second level when the data is collected from the physical copy relating to the performance, robustness, and maintenance. The virtual model uses the collected data to assist in the design and development of the physical system along with decision-making and arranging maintenance.
- Adaptive Digital Twin is third level which imparts an adaptive interface between the physical and the digital world. Using ML techniques, it learns from the experiences of the human operators, allowing for real-time decision-making.
- Intelligent Digital Twin has additional capabilities along with features from the second and third levels. It can detect patterns in the manufacturing floor using reinforcement learning, allowing for more precision and efficient handling of the system.
3.2. Edge Computing: Enabling the Digital Twin
- Interoperability—provides the required protocol conversion for communications to be acknowledged between devices unable to communicate with each other.
- Localised processing—facilitates the unloading of computing jobs from smart devices by caching information and functioning as a private cloud suitable for remote access.
- Quality of service—increases the efficacy of available network bandwidth while decreasing endpoint bottlenecks.
- Security—provides advanced security solutions compared to those implemented on each endpoint, hence building a better defensive strategy for the entire network within the factory.
- Local storage—saves transmission costs by just transmitting the necessary data to the cloud. In certain cases, it is advantageous to have the edge device act as the computing node to record data and make localised analytical decisions.
3.3. Barriers to Digital Twin Integration in the Smart Manufacturing Sector
- Preventive maintenance: IoT capabilities will improve operational intelligence, which is essential to smart manufacturing. This sector will gain from the availability of numerous sensors able to provide real-time information regarding equipment performance. The data can aid in predicting and preventing equipment malfunction when integrated with machine learning (ML) and artificial intelligence (AI).
- Enhanced process efficiencies and troubleshooting: Interoperability and digital transformation work together to improve manufacturing process efficiencies. For example, using deep learning neural networks and advanced visual recognition, robotic systems can accurately and quickly scan connected objects for quality control in real time. Specialised equipment can be fixed remotely by specialists using augmented reality (AR), made possible by 5G networks’ high bandwidth and low latency support.
- Increased security with built-in security features: Interoperability and digital transformation will provide increased security with built-in security features, integrating security into the core network architecture and allaying manufacturers’ security fears about adopting IoT .
- Sandbox scenario testing: The charm of digital twinning is that it enables businesses to construct specialised, virtual replicas of their web infrastructure and to do predictive research tailored to their requirements. Employing digital twins to assess the viability of system applications and data architecture can help businesses intending to implement new systems or migrate to the cloud.
- System Scalability: There is little doubt that there are huge opportunities for large-scale digital twins, which can assist manufacturing sites greatly by enhancing asset maintenance, increasing company transparency, and improving and deepening decision-making.
4. Digital Twins: Enabling Interoperability in Manufacturing
- Predictive maintenance and equipment safety—e.g., a pump installed with edge computing capabilities can determine if an established threshold has been surpassed, using basic analytics, and shut itself in milliseconds. Applying an edge computing device to perform this function will result in zero decision latency without any requirement for internet connectivity.
- Production flow monitoring and optimisation––to compile the data on a local gateway and send overall equipment effectiveness (OEE) patterns and alerts to the operational staff, edge computing can do near real-time analytics across a variety of data obtained from sensors installed within the floor.
- Supply chain optimisation—any industrial facility’s supply chain processes must be optimised, which calls for the use of optimisation algorithms and data analytics that can quickly adjust supply-chain goals inside business systems such as ERP, SCM, etc.
Machine Learning in Smart Manufacturing
- Predictive maintenance. The amount of downtime can be considerably decreased by using historical data from maintenance logs to estimate how a machine will react under a future payload and whether it will need to be changed based on what previously resolved that issue.
- Predictive quality. Significant cost savings can be achieved by anticipating and reducing costs.
- Scrap reduction. It is possible to reduce waste and increase product quality by using measurements to predict behaviour across product requirements.
- Increasing yield/throughput. Knowing if and when a machine or process will not conform to a set of requirements enables proactive action to be taken to bring it back into compliance, lowering the number of quality passes.
5. A Digital Twin Framework for Interoperability
- Improved supply chain management, resulting in reduced costs, increased efficiency, and faster delivery times .
- Enhanced quality control processes, leading to improved product quality, reduced waste, and increased customer satisfaction .
- Increased regulatory compliance, through standardized data collection, sharing, and analysis, reducing costs and improving transparency .
- Informed decision-making through better data analytics, providing insights into operations, products, and customers .
- Greater innovation and collaboration, enabling the development of new products, services, and business models that meet evolving market needs .
Data Availability Statement
Conflicts of Interest
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O’Connell, E.; O’Brien, W.; Bhattacharya, M.; Moore, D.; Penica, M. Digital Twins: Enabling Interoperability in Smart Manufacturing Networks. Telecom 2023, 4, 265-278. https://doi.org/10.3390/telecom4020016
O’Connell E, O’Brien W, Bhattacharya M, Moore D, Penica M. Digital Twins: Enabling Interoperability in Smart Manufacturing Networks. Telecom. 2023; 4(2):265-278. https://doi.org/10.3390/telecom4020016Chicago/Turabian Style
O’Connell, Eoin, William O’Brien, Mangolika Bhattacharya, Denis Moore, and Mihai Penica. 2023. "Digital Twins: Enabling Interoperability in Smart Manufacturing Networks" Telecom 4, no. 2: 265-278. https://doi.org/10.3390/telecom4020016